Association between vaccination and days of hospitalization in adult patients with non-severe COVID-19.

Introduction: To explore the association between vaccination status and the days of hospitalization in non-severe adult COVID-19 patients.

Methodology: We retrospectively analyzed the 368 non-severe adult COVID-19 patients which were divided into three groups according to their vaccination status. Univariate and multivariate linear regression analysis were performed to determine the correlation between vaccination and the days of hospitalization.

Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods.

Based on the patient's clinical characteristics and laboratory indicators, different machine-learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID-19) patients. A retrospective analysis was performed on 376 nonsevere COVID-19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67).

Association between squamous cell carcinoma antigen level and EGFR mutation status in Chinese lung adenocarcinoma patients.

Background: To investigate the association between squamous cell carcinoma antigen (SCCAg) level and epidermal growth factor receptor (EGFR) mutation status in Chinese lung adenocarcinoma patients.

Methods: We retrospectively analyzed 293 patients with lung adenocarcinoma, divided into EGFR mutant group (n = 178) and EGFR wild-type group (n = 115). The general data and laboratory parameters of the two groups were compared.

Machine Learning Algorithms are Superior to Conventional Regression Models in Predicting Risk Stratification of COVID-19 Patients.

Background: It is very important to determine the risk of patients developing severe or critical COVID-19, but most of the existing risk prediction models are established using conventional regression models. We aim to use machine learning algorithms to develop predictive models and compare predictive performance with logistic regression models.

Methods: The medical record of 161 COVID-19 patients who were diagnosed January-April 2020 were retrospectively analyzed.

Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID-19.

In the present study, a prediction model with combined laboratory indexes in risk stratification of patients with COVID-19 was established and tested. The data of 170 patients with COVID-19 who were divided into an asymptomatic-moderate group (141 cases) and severe or above group (29 cases) were retrospectively analyzed. The clinical characteristics and laboratory indexes of the two groups were compared.

Erratum: Long Noncoding RNA (lncRNA) Maternally Expressed Gene 3 (MEG3) Participates in Chronic Obstructive Pulmonary Disease through Regulating Human Pulmonary Microvascular Endothelial Cell Apoptosis.

Figure 3 was incorrectly published in the article titled Long Noncoding RNA (lncRNA) Maternally-Expressed Gene 3 (MEG3) Participates in Chronic Obstructive Pulmonary Disease Through Regulating Human Pulmonary Microvascular Endothelial Cell Apoptosis; PMID: 32201430; PMCID: PMC7111098; DOI: 10.12659/MSM.920793.

Long Noncoding RNA (lncRNA) Maternally Expressed Gene 3 (MEG3) Participates in Chronic Obstructive Pulmonary Disease through Regulating Human Pulmonary Microvascular Endothelial Cell Apoptosis.

BACKGROUND Chronic obstructive pulmonary disease (COPD), a general airway disease, is featured by progressive and chronic immunoreaction in the lung. Increasing evidences have showed that cigarette smoking is the main reason in the COPD progression, and human pulmonary microvascular endothelial cell (HPMEC) apoptosis often be observed in COPD, while its pathogenesis is not yet fully described. Upregulation of long noncoding RNA (lncRNA) maternally expressed gene 3 (MEG3) was observed in COPD patients, but the specific mechanism of lncRNA MEG3 in COPD remains unknown.

[Predictive value of procalcitonin in intensive care unit delirium].

Objective: To analyze the risk factors of delirium in intensive care unit (ICU) patients, and to investigate the predictive value of C-reactive protein (CRP), procalcitonin (PCT), lactic acid (Lac) and neuron-specific enolase (NSE) in the diagnosis of ICU delirium.

Methods: The patients admitted to central ICU and respiratory medicine ICU of Changzhou First People's Hospital from August 2016 to November 2017 were enrolled. The patients were divided into two groups according to whether delirium occurred within 7 days or not, which was evaluated by using the confusion assessment method for ICU (CAM-ICU).

5-Aza-2'-deoxycytidine protects against emphysema in mice via suppressing p16 expression in lung tissue.

Background: There is a growing realization that COPD, or at least emphysema, involves several processes presenting in aging and cellular senescence. Endothelial progenitor cells (EPCs) contribute to neovascularization and play an important role in the development of COPD. The gene for p16 is a major dominant senescence one.

Long Noncoding RNA Bladder Cancer Associated Transcript 1 Promotes the Proliferation, Migration, and Invasion of Nonsmall Cell Lung Cancer Through Sponging miR-144.

Nonsmall cell lung cancer (NSCLC) is a type of malignant tumor, accounting for 80% of all lung cancer morbidity. Long noncoding RNA (lncRNA) BLACAT1 (bladder cancer associated transcript 1), also known as linc-UBC1, has been tested to be an oncogenic lncRNA in other cancers. However, the role of BLACAT1 in NSCLC is still unknown.

Decitabine enhances stem cell antigen-1 expression in cigarette smoke extract-induced emphysema in animal model.

Stem cell antigen-1 (Sca-1) is a mouse glycosyl phosphatidylinositol-anchored protein and a cell surface marker found on hematopoietic stem cells (HSCs). Despite decades of study, its biological functions remain little known. Sca-1 is a typical marker of bone marrow-derived HSCs, it is also expressed by a mixture of tissue-resident stem, progenitor cells in nonhematopoietic organs.

Comparison between cigarette smoke-induced emphysema and cigarette smoke extract-induced emphysema.

Background And Objective: Emphysema is the main pathological feature of COPD and also is the focus of the related research. Although several emphysema animal models have been established, exact comparison of findings is seldom. The present study aimed to compare cigarette smoke (CS) exposure-induced emphysema model and intraperitoneal injection of cigarette smoke extract (CSE)-induced emphysema model to evaluate the effectiveness of the two different modeling methods.

Dual effects of cigarette smoke extract on proliferation of endothelial progenitor cells and the protective effect of 5-aza-2'-deoxycytidine on EPCs against the damage caused by CSE.

Unlabelled: Cigarette smoke is a major public health problem associated with multitude of diseases, including pulmonary and vascular diseases. Endothelial progenitor cells (EPCs) contribute to neovascularization and play an important role in the development of these diseases. The effect of CSE on EPCs is seldom studied.

C-Kit/c-Kit ligand interaction of bone marrow endothelial progenitor cells is influenced in a cigarette smoke extract-induced emphysema model.

Background: Smoking causes lung endothelial cell apoptosis and emphysema. Derived from bone marrow, circulating endothelial progenitor cells (EPCs) maintain vascular integrity by replacing and repairing damaged endothelial cells. Smoking influences the number of circulating EPCs.