Diabetes-induced tissue injuries in target organs such as the kidney, heart, eye, liver, skin, and nervous system contribute significantly to the morbidity and mortality of diabetes. However, whether the lung should be considered a diabetic target organ has been discussed for decades. Accumulating evidence shows that both pulmonary histological changes and functional abnormalities have been observed in diabetic patients, suggesting that the lung is a diabetic target organ. Mechanisms underlying diabetic lung are unclear, however, oxidative stress, systemic inflammation, and premature aging convincingly contribute to them. Circadian system and Sirtuins have been well-documented to play important roles in above mechanisms. Circadian rhythms are intrinsic mammalian biological oscillations with a period of near 24 h driven by the circadian clock system. This system plays an important role in the regulation of energy metabolism, oxidative stress, inflammation, cellular proliferation and senescence, thus impacting metabolism-related diseases, chronic airway diseases and cancers. Sirtuins, a family of adenine dinucleotide (NAD +)-dependent histone deacetylases, have been demonstrated to regulate a series of physiological processes and affect diseases such as obesity, insulin resistance, type 2 diabetes (T2DM), heart disease, cancer, and aging. In this review, we summarize recent advances in the understanding of the roles of the circadian clock and Sirtuins in regulating cellular processes and highlight the potential interactions of the circadian clock and Sirtuins in the context of diabetic lung.
IntroductionThe loss of blood is a significant problem in Total Knee Arthroplasty (TKA). Anemia often occurs after such surgeries, leading to serious consequences, such as higher postoperative infection rates and longer hospital stays. Tools for predicting possible anemia can provide additional guidance in realizing better blood management of patients.Methods2,165 patients who underwent TKA from 2015 to 2019 in the same medical center were divided into training and validation cohorts. Both univariate and multivariate logistic regression analyses were performed to identify independent preoperative risk factors for anemia. Based on these predictors, a nomogram was established using the area under the curve (AUC), calibration curve (AUC), and the area under the curve (AUC). The model was then applied to the validation cohort, and decision curve analyses (DCA) were also plotted.ResultsThrough analysis of both univariate and multivariate logistic regression, five independent predictors were found in the training cohort: female, relatively low BMI, low levels of preoperative hemoglobin, abnormally high levels of ESR, and simultaneously two sides of TKA in the same surgery. The AUCs of the nomogram were 74.6% (95% CI, 71.35%–77.89%) and 68.8% (95% CI, 63.37%–74.14%) of training and the validation cohorts separately. Furthermore, the calibration curves of both cohorts illustrated the consistency of the nomogram with the actual condition of anemia of patients after TKA. The DCA curve was higher for both treat-none and treat-all, further indicating the relatively high practicality of the model.ConclusionFemale, lower BMI, lower levels of preoperative Hb, simultaneous bilateral TKA, and high levels of preoperative ESR were figured out as five independent risk factors for postoperative anemia (<9.0 g/dL) in patients undergoing TKA. Based on the findings, a practical nomogram was constructed to predict risk of postoperative anemia. The evidence level should be level 4 according to guideline.
Background The early prediction of intravenous corticosteroid (IVCS) resistance in acute severe ulcerative colitis (ASUC) patients remains an unresolved challenge. This study aims to construct and validate a model that accurately predicts IVCS resistance. Methods A retrospective cohort was established, with consecutive inclusion of patients who met the diagnosis criteria of ASUC and received IVCS during index hospitalization in Peking Union Medical College Hospital between March 2012 and January 2020. The primary outcome was IVCS resistance. Classification models, including logistic regression and machine learning-based models, were constructed. External validation was conducted in an independent cohort from Shengjing Hospital of China Medical University. Results A total of 129 patients were included in the derivation cohort. During index hospitalization, 102 (79.1%) patients responded to IVCS and 27 (20.9%) failed; 18 (14.0%) patients underwent colectomy in 3 months; 6 received cyclosporin as rescue therapy, and 2 eventually escalated to colectomy; 5 succeeded with infliximab as rescue therapy. The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and C-reactive protein (CRP) level at Day 3 are independent predictors of IVCS resistance. The areas under the receiver-operating characteristic curves (AUROCs) of the logistic regression, decision tree, random forest, and extreme-gradient boosting models were 0.873 (95% confidence interval [CI], 0.704–1.000), 0.648 (95% CI, 0.463–0.833), 0.650 (95% CI, 0.441–0.859), and 0.604 (95% CI, 0.416–0.792), respectively. The logistic regression model achieved the highest AUROC value of 0.703 (95% CI, 0.473–0.934) in the external validation. Conclusions In patients with ASUC, UCEIS and CRP levels at Day 3 of IVCS treatment appeared to allow the prompt prediction of likely IVCS resistance. We found no evidence of better performance of machine learning-based models in IVCS resistance prediction in ASUC. A nomogram based on the logistic regression model might aid in the management of ASUC patients.
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