Medicine
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This study aimed to analyze the risk factors for postoperative lung infection in elderly patients with lung cancer (LC) and construct a predictive model. A retrospective analysis was conducted on 192 elderly patients with LC who underwent surgical treatment in our hospital between February 2020 and May 2023. According to whether there is lung infection after surgery, they were divided into an infected group (n = 55) and a noninfected group (n = 137). ⋯ Receiver operating characteristic curve analysis showed that the area under curve values of CRP, IL-6, IGF-1, and their combination in predicting postoperative lung infection in elderly patients with LC were 0.701, 0.806, 0.737, and 0.871, P < .05), with sensitivity values of 0.443, 0.987, 0.456, and 0.835, respectively; the specificity was 0.978, 0.525, 0.991, and 0.821, respectively. Age > 70 years, smoking history, diabetes history, prolonged use of perioperative antibiotics, and elevated CRP, IL-6, and IGF-1 levels on the 1st day after surgery have an impact on postoperative lung infection in elderly patients with LC. Early postoperative monitoring of changes in CRP, IL-6, and IGF-1 levels can provide an important reference for predicting the occurrence of postoperative lung infections.
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Observational Study
Role of Charlson comorbidity index in predicting intensive care unit readmission in patients with aortic aneurysm.
The purpose of this study was to investigate the value of the Charlson comorbidity index (CCI) in predicting intensive care unit (ICU) readmission in aortic aneurysm (AA) patients. Patient information came from the Medical Information Mart for Intensive Care- IV (MIMIC-IV) database. The relationship between CCI and ICU readmission was analyzed by restricted cubic spline, generalized linear regression, trend analysis, and hierarchical analysis. ⋯ Further, CCI was found to have better clinical value in predicting ICU readmission of thoracic aortic aneurysm (TAA) patients undergoing surgery. Age, renal disease, chronic lung disease, and dementia were important components of CCI in predicting ICU readmission of TAA patients undergoing surgery. CCI was independently associated with the ICU readmission of AA patients in a positive relationship and had more favorable prediction performance in TAA patients who underwent surgery.
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The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma. We included patients who underwent craniotomy and evacuation of hematoma due to traumatic brain injury in our hospital from January 2015 to December 2020. Ten machine learning methods were selected to model prediction, including XGBoost, Logistic Regression, Light GBM, Random Forest, AdaBoost, GaussianNB, ComplementNB, Support Vector Machines, and KNeighbors. ⋯ The AdaBoost model showed an AUC of 0.909 (95% CI, 0.849-0.970) in the validation cohort. Although there was an underestimated acute kidney injury risk for the model in the calibration curve, there was a net benefit for the AdaBoost model in the decision curve. Our machine learning model was evaluated to have a good performance in the validation cohorts and could be a useful tool in the clinical practice.
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This study aims to investigate the factors affecting the stone-free rate (SFR) of flexible ureteroscopy and laser lithotripsy (fURSL) for renal stones and establish predictive models by identifying their prognostic factors. We retrospectively examined 252 patients with renal stones who were treated with fURSL between July 2020 and April 2022. We analyzed the relationship between the patient's clinical data (sex, age, and body mass index), stone status (side, size, location, stone/transverse process pixel ratio [STPR], and the CT value of stone [SCTV]), and SFR to determine the relevant factors and analyze their influence. ⋯ After 1000 resamples and internal self-validation, the C-indices of models 1 and 2 were 0.924 and 0.895, respectively, showing that the stone clearance predicted by the nomogram matched the actual situation. Stone location, size, and density (SCTV and STPR) were significant predictors of SFR after fURSL. The scoring system based on these factors may be used to guide optimal treatment strategy selection.
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Observational Study
Lnc NEAT1 facilitates the progression of melanoma by targeting the miR-152-3p/CDK6 axis: An observational study.
Long noncoding (Lnc) RNAs are novel regulators in melanoma. Lnc nuclear enriched autosomal transcript 1 (NEAT1) was reportedly upregulated in melanoma; however, the functional roles and mechanisms of Lnc NEAT1 need further investigation. Therefore, we used quantitative real-time PCR to determine the mRNA levels of Lnc NEAT1, miR-152-3p, and cyclin-dependent protein kinase 6 (CDK6). ⋯ The underlying mechanism is that Lnc NEAT1 serves as a sponge for miR-152-3p to suppress the inhibitory effect of miR-152-3p on CDK6. Furthermore, the miR-152-3p/ CDK6 axis was implicated in the progression of melanoma accelerated by Lnc NEAT1. Taken together, Lnc NEAT1 may promote melanoma development by serving as an endogenous sponge of miR-152-3p, increasing CDK6 expression, and identifying a new target for the treatment of melanoma.