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Aging is a complex biological process characterized by the gradual decline of cellular functions, increased susceptibility to diseases, and impaired stress responses. Hypoxia, defined as reduced oxygen availability, is a critical factor that influences aging through molecular pathways involving hypoxia‐inducible factors (HIFs), oxidative stress, inflammation, and epigenetic modifications. This review explores the interconnected roles of hypoxia in aging, highlighting how hypoxic conditions exacerbate cellular damage, promote senescence, and contribute to age‐related pathologies, including cardiovascular diseases, neurodegenerative disorders, cancer, metabolic dysfunctions, and pulmonary conditions. By examining the molecular mechanisms linking hypoxia to aging, we identify key pathways that serve as potential therapeutic targets. Emerging interventions such as HIF modulators, antioxidants, senolytics, and lifestyle modifications hold promise in mitigating the adverse effects of hypoxia on aging tissues. However, challenges such as the heterogeneity of aging, lack of reliable biomarkers, and safety concerns regarding hypoxia‐targeted therapies remain. This review emphasizes the need for personalized approaches and advanced technologies to develop effective antiaging interventions. By integrating current knowledge, this review provides a comprehensive framework that underscores the importance of targeting hypoxia‐induced pathways to enhance healthy aging and reduce the burden of age‐related diseases. [ABSTRACT FROM AUTHOR]

Background: Achieving endoscopic and histological remission is a critical treatment objective in ulcerative colitis (UC). Nevertheless, interobserver variability can significantly impact overall assessment performance. Objectives: We aimed to develop a deep learning algorithm for the real-time and objective evaluation of endoscopic disease activity and prediction of histological remission in UC. Design: This is a retrospective diagnostic study. Methods: Two convolutional neural network (CNN) models were constructed and trained using 12,257 endoscopic images and biopsy results sourced from 1124 UC patients who underwent colonoscopy at a single center from January 2018 to December 2022. Mayo Endoscopy Subscore (MES) and UC Endoscopic Index of Severity Score (UCEIS) assessments were conducted by two experienced and independent reviewers. Model performance was evaluated in terms of accuracy, sensitivity, and positive predictive value. The output of the CNN models was also compared with the corresponding histological results to assess histological remission prediction performance. Results: The MES-CNN model achieved 97.04% accuracy in diagnosing endoscopic remission of UC, while the MES-CNN and UCEIS-CNN models achieved 90.15% and 85.29% accuracy, respectively, in evaluating endoscopic severity of UC. For predicting histological remission, the CNN models achieved accuracy and kappa values of 91.28% and 0.826, respectively, attaining higher accuracy than human endoscopists (87.69%). Conclusion: The proposed artificial intelligence model, based on MES and UCEIS evaluations from expert gastroenterologists, offered precise assessment of inflammation in UC endoscopic images and reliably predicted histological remission. Plain language summary: Application of deep learning in the diagnosis and evaluation of ulcerative colitis disease severity Why was this study done? This study aimed to develop a real-time and objective diagnostic tool to reduce subjectivity when evaluating ulcerative colitis (UC) endoscopic disease activity and to predict histological remission without mucosal biopsy. What did the researchers do? We developed and validated a deep learning algorithm that uses UC endoscopic images to predict the Mayo Endoscopic Score (MES), US Endoscopic Index of Severity Score (UCEIS), and histological remission. What did the researchers find? The constructed MES- and UCEIS-based models both achieved high accuracy and performance in predicting histological remission, outperforming human endoscopists. What do the findings mean? The efficiency and performance of the deep learning algorithm rivaled that of expert assessments, which may assist endoscopists in making more objective evaluations of UC severity and in predicting histological remission. [ABSTRACT FROM AUTHOR]

Background: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images., Methods: The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared., Results: In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98)., Conclusions: The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Plenty of molecular circuits with specific functions have been developed; however, logic units with reconfigurability, which could simplify the circuits and speed up the information process, are rarely reported. In this work, we designed a novel reconfigurable logic unit based on a DNA-templated, potassium-concentration-dependent, supramolecular assembly, which could respond to the input stimuli of H + and K + . By inputting different concentrations of K + , the logic unit could implement three significant functions, including a half adder, a half subtractor, and a 2-to-4 decoder. Considering its reconfigurable ability and good performance, the novel prototypes developed here may serve as a promising proof of principle in molecular computers., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Inflammatory bowel disease (IBD) includes ulcerative colitis (UC) and Crohn's disease (CD). Glucocorticoids (GCs) are the most effective treatment for moderate to severe active UC. However, one-third of patients are not sensitive to GCs (i.e., they are GC resistant). The mechanism of GC resistance in IBD is unknown, and it remains unclear how to predict resistance in IBD patients. This study aimed to explore the possible correlation between miRNA expression and variability in GC-resistant and GC-sensitive patients with ulcerative colitis. A comparative serum microRNA analysis in GC-resistant and GC-sensitive patients with ulcerative colitis was conducted by microarray. Differential microRNA expression was further validated in serum samples by quantitative real-time PCR. We found that downregulated microRNAs had a significant correlation with several signal transduction pathways (the PI3K-Akt and MAPK signaling pathways) and target genes ( HSP90B1, MAPK13, MAPK9, PIK3AP1 and TLR4 ) related to GC resistance. Eight downregulated microRNAs were chosen for further validation in 76 serum samples. The results showed that miR-16-2-3p, miR-30e-3p, miR-32-5p, miR-642a-5p, miR-150-5p, and miR-224-5p were significantly downregulated in the GC-resistant group. Receiver operating characteristic analysis showed that the area under the curves (AUCs) for those microRNAs were 0.94, 0.93, 0.85, 0.87, 0.92, and 0.99, with specificities of 97.30%, 89.20%, 59.50%, 73.00%, 97.30%, and 97.30% and sensitivities of 74.40%, 84.60%, 97.40%, 92.30%, 66.70%, and 89.70%, respectively. Our study provides preliminary evidence for the pathogenic mechanism of GC resistance and shows that serum microRNAs might serve as biomarkers for GC resistance in IBD., Competing Interests: None., (IJCEP Copyright © 2018.)