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. A generalized additive model and hierarchical linear regression model were used for outcome analysis. RESULTS: In the regression equation, the increase in the number of vaccine shots was significantly correlated with the decrease in the days of hospitalization (all p < 0.001). Particularly, the reduction of the days of hospitalization in patients with 3 injections of the vaccine was more significant than that of the 0-1 injection group (ß: -2.810, -2.525, and -2.831; p < 0.001). Curve fitting showed that the relationship between the number of vaccination injections and the days of hospitalization was approximately linear, and the ß value was -1.522 (95% CI: -2.091 - -0.954; p < 0.001). Among various laboratory indexes, only the monocyte ratio significantly affected the correlation between the number of vaccination injections and the days of hospitalization, indicating an interaction (p =0.027). The ß values of the monocyte ratio in normal and elevated groups were -2.230 (95% CI: -3.048 - -1.412; p < 0.001) and -0.763 (95% CI: -1.520 - -0.005; p = 0.050), respectively. CONCLUSIONS: In non-severe adult COVID-19 patients, there was a negative linear correlation between the vaccination status 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). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K-Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non-severe COVID-19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic.

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. We used univariable and multivariable logistic regression to analyze the association between SCCAg level and EGFR mutation. Generalized additive model was used for curve fitting, and a hierarchical binary logistic regression model was used for interaction analysis. RESULTS: Squamous cell carcinoma antigen level in the EGFR wild-type group was significantly higher than that in the mutant group (p < 0.001). After adjusting for confounding factors, we found that elevated SCCAg was associated with a lower probability of EGFR mutation, with an OR of 0.717 (95% CI: 0.543-0.947, p = 0.019). For the tripartite SCCAg groups, the increasing trend of SCCAg was significantly associated with the decreasing probability of EGFR mutation (p for trend = 0.015), especially for Tertile 3 versus Tertile 1 (OR = 0.505; 95% CI: 0.258-0.986; p = 0.045). Curve fitting showed that there was an approximate linear negative relationship between continuous SCCAg and EGFR mutation probability (p = 0.020), which was first flattened and then decreased (p < 0.001). The association between the two was consistent among different subgroups, suggesting no interaction (all p > 0.05). CONCLUSION: There is a negative association between SCCAg level and EGFR mutation probability in Chinese lung adenocarcinoma patients.

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. The patients were divided into two groups: asymptomatic-moderate group (132 cases) and severe or above group (29 cases). The clinical features and laboratory biomarkers of these two groups were compared. Machine learning algorithms and multivariate logistic regression analysis were used to construct two COVID-19 risk stratification prediction models, and the area under the curve (AUC) was used to compare the predictive efficacy of these two models. RESULTS: A machine learning model was constructed based on seven characteristic variables: high sensitivity C-reactive protein (hs-CRP), procalcitonin (PCT), age, neutrophil count (Neuc), hemoglobin (HGB), percentage of neutrophils (Neur), and platelet distribution width (PDW). The AUC of the model was 0.978 (95% CI: 0.960-0.996), which was significantly higher than that of the logistic regression model (0.827; 95% CI: 0.724-0.930) (P=0.002). Moreover, the machine learning model's sensitivity, specificity, and accuracy were better than those of the logistic regression model. CONCLUSION: Machine learning algorithms improve the accuracy of risk stratification in patients with COVID-19. Using detection algorithms derived from these techniques can enhance the identification of critically ill patients.

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. Multivariate logistic regression analysis was performed to construct the prediction model based on laboratory indexes. A receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of different indexes. Decision curve analysis (DCA) was performed to quantify and compare the clinical validity of the prediction models. There were significant differences in blood cell count, high-sensitivity C-reactive protein (hsCRP) and procalcitonin (PCT) levels between the severe or above group and the asymptomatic-moderate group (all P<0.05). Among all individual indexes, hsCRP had the highest diagnostic efficacy (area under the curve=0.870), with a sensitivity and specificity of 0.828 and 0.802, respectively. The red blood cell count, hsCRP and PCT were used to construct the prediction model. The AUC of the prediction model was higher than that of hsCRP (0.912 vs. 0.870) but the difference was not significant (P=0.307). DCA suggested that the net benefit of the prediction model was higher than that of hsCRP in most cases and significantly higher than that of PCT, lymphocytes and monocytes. The prediction model with combined laboratory indexes was able to more effectively predict the clinical classification of patients with COVID-19 and may be used as a tool for risk stratification of patients.

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. The correct Figure 3 is as follows.

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. The objective of this research was to explore the role of lncRNA MEG3 in cigarette smoke extract (CSE)-induced HPMECs. MATERIAL AND METHODS HPMECs were induced by a series of concentrations of CSE (0%, 0.1%, 1%, and 10%). Then cell apoptosis was analyzed by flow cytometry. Cell apoptosis related proteins were tested using western blot assay. Finally, we applied knockdown and over-expression system to explore the lncRNA MEG3 functions in CSE-induced HPMECs. RESULTS Our results indicated that various concentrations of CSE (0%, 0.1%, 1%, and 10%) significantly promoted cell apoptosis, augmented caspase-3 activity, upregulated Bax expression, decreased Bcl-2 expression, and enhanced lncRNA MEG3 level in HPMECs. LncRNA MEG3-plasmid transfection resulted in the upregulation of lncRNA MEG3, more apoptotic HPMECs, and higher caspase-3 activity. While lncRNA MEG3 knockdown presented the opposite effects. Further investigation suggested that all the effects of CSE treatment on HPMECs were markedly reversed by lncRNA MEG3-shRNA (short hairpin RNA). CONCLUSIONS Our study illustrated a protective effect of lncRNA MEG3-shRNA on CSE-induced HPMECs, indicting lncRNA MEG3 can be a new therapeutic approach for COPD treatment.

[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). The gender, age and blood CRP, PCT, Lac, NSE levels were compared between the two groups. Multivariate Logistic regression model was used to analyze the risk factors of ICU delirium. Receiver operating characteristic curve (ROC) was drawn to assess the predictive value of CRP, PCT, Lac and NSE in the occurrence of ICU delirium. RESULTS: 133 patients were enrolled. Delirium occurred in 67 patients, and did not occurred in 66 patients, with a prevalence rate of 50.4%. (1) There was no significant difference in gender or age between the two groups. Compared with non-delirium group, blood CRP, PCT and Lac levels in delirium group were significantly increased [CRP (mg/L): 110.75±77.31 vs. 51.32±36.51, PCT (µg/L): 3.95 (1.01, 23.90) vs. 0.09 (0.06, 0.36), Lac (mmol/L): 2.40 (1.70, 4.30) vs. 1.20 (0.90, 2.00), all P < 0.01], but no significant difference was found in NSE [µg/L: 12.59 (9.61, 17.69) vs. 13.39 (10.14, 19.05), P > 0.05]. (2) It was shown by multivariate Logistic regression analysis that blood PCT and Lac were risk factors of ICU delirium [PCT: odds ratio (OR) = 1.185, 95% confidence interval (95%CI) = 1.006-1.396, P = 0.042; Lac: OR = 1.398, 95%CI = 1.011-1.934, P = 0.043]. (3) ROC curve analysis showed that blood CRP, PCT and Lac had certain predictive value for ICU delirium, and the area under the ROC curve (AUC) of PCT was the highest (0.840 vs. 0.694 and 0.751). When the cut-off value of PCT ≥ 0.55 µg/L, the sensitivity was 72.7%, the specificity was 86.2%, positive predictive value was 84.48%, and negative predictive value was 75.68%. Blood NSE had no predictive value for ICU delirium (AUC = 0.446, P = 0.290). CONCLUSIONS: Blood PCT and Lac are the risk factors of ICU delirium. PCT has predictive value for ICU delirium.

5-Aza-2'-deoxycytidine protects against emphysema in mice via suppressing p16Ink4a 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 p16Ink4a is a major dominant senescence one. The aim of the present study was to observe changes in lung function, histomorphology of lung tissue, and expression of p16Ink4a in lung tissue and bone marrow-derived EPCs in emphysematous mice induced by cigarette-smoke extract (CSE), and further to search for a potential candidate agent protecting against emphysema induced by CSE. MATERIALS AND METHODS: An animal emphysema model was induced by intraperitoneal injection of CSE. 5-Aza-2'-deoxycytidine (5-Aza-CdR) was administered to the emphysematous mice. Lung function and histomorphology of lung tissue were measured. The p16Ink4a protein and mRNA in EPCs and lung tissues were detected using Western blotting and quantitative reverse-transcription polymerase chain reaction, respectively. RESULTS: CSE induced emphysema with increased p16Ink4a expression in lung tissue and bone marrow-derived EPCs. 5-Aza-CdR partly protected against emphysema, especially in the lung-morphology profile, and partly protest against the overexpression of p16Ink4a in EPCs and lung tissue induced by CSE. CONCLUSION: 5-Aza-CdR partly protected against emphysema in mice via suppressing p16Ink4a expression in EPCs and lung tissue.

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. In clinical study, BLACAT1 expression was significantly upregulated in 48 cases of NSCLC tissues compared with adjacent normal tissues, especially in pathological TNM III samples. In NSCLC cells, BLACAT1 expression was also upregulated. Both in vivo and in vitro, BLACAT1 silencing transfected with si-BLACAT1 could suppress proliferation, migration, and invasion, and induce G0/G1 phase arrest. Bioinformatics methods and luciferase reporter assay revealed the close link within miR-144 and BLACAT1 3'-untranslated region (UTR). Furthermore, combining experiments of miR-144 and BLACAT1 indicated that miR-144 could reverse the function of BLACAT1 on NSCLC cells' phenotype. Overall, this study reveals the overexpression of BLACAT1 in NSCLC tissue and cells, and discovers the oncogenic role of BLACAT1 in NSCLC genesis through sponging miR-144, providing a potential biomarker for early detection and prognosis prediction of NSCLC.

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. Endothelial progenitor cell (EPC) is a subtype of HSC and contributes to endothelial repair by homing in on locations of injury. Abnormal genetic methylation has been detected in smoking-related diseases. The present study aimed to investigate the lung function and histomorphology, the expression of Sca-1 gene in lung tissues, and bone marrow-derived EPCs in cigarette smoke extract (CSE)-induced emphysema mice, and to further determine whether Decitabine (Dec), the most widely used inhibitor of DNA methylation, could protect against the damages caused by CSE. The results of the present study demonstrated that Dec could partly protect against CSE-induced emphysema in mice, enhance Sca-1 expression in lung tissue, and bone marrow-derived EPCs. The results suggested that the depletion of the progenitor cell pool and DNA methylation of Sca-1 gene may be involved in the progression of emphysema in mice.

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. METHODS: Six-week-old male C57BL/6 J mice were used and randomly divided into two groups: CS exposure and intraperitoneal injection of CSE. Each group was subdivided into two subgroups: control and CS or CSE. Lung function, mean linear intercept (MLI), destructive index (DI), apoptotic index (AI), total and differential cells count in broncholavolar lavage fluid (BALF), SOD and IL-6 concentration in serum were measured. RESULTS: Compared with their respective controls, lung function was significantly decreased in CS and CSE groups (P < 0.01); MLI, DI, and AI of lung tissue were significantly higher in CS and CSE groups (P < 0.01); total number of leukocytes, the number and percentage of neutrophils (NEUs), and the number of macrophages (MAC) in BALF were significantly higher in CS and CSE groups (P < 0.01); SOD concentration in serum was significantly decreased in CS and CSE groups (P < 0.01); IL-6 concentration in serum was significantly increased in in CS and CSE groups (P < 0.01). There was no significant difference between CS group and CSE group in any of the parameters described above. CONCLUSIONS: Both CS exposure and intraperitoneal injection of CSE could induce emphysema and the effectiveness of the two different modeling methods were equal.

Diagnostic value of surfactant protein-a in severe acute pancreatitis-induced acute respiratory distress syndrome.

BACKGROUND: The complexity of multiple-item criteria in acute respiratory distress syndrome (ARDS) often causes inconvenience for physicians in the management of patients with severe acute pancreatitis (SAP). We evaluated whether serum SP-A levels in the presence of diffuse alveolar damage (DAD) can be qualitatively assessed for diagnosis of SAP-induced ARDS. MATERIAL AND METHODS: Eighty rats were randomly divided into 2 groups (n=40 each) - the sham-operated (SO) group and the SAP group - and then randomly subdivided into 4 subgroups in a time-course manner. Furthermore, rats in the SAP group were subdivided into the SAP induced-ARDS group (ARDS group) and the SAP without ARDS group (non-ARDS group) according to the diagnostic standard of ARDS. The diagnostic cut-off values of SP-A for SAP-induced ARDS were determined by the receiver operating characteristic curve (ROC). RESULTS: Serum SP-A levels in Baseline, SO group, SAP group, ARDS group, and non-ARDS group were 43.15±14.29, 51.91±16.99, 193.4±35.37, 198.0+29.73, and 185.7±43.21 ug/ml, respectively. The best cut-off value for the serum SP-A level for the diagnosis of SAP-induced ARDS was 150 ug/ml and the area under the ROC curve of SP-A was 0.88. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of SP-A in the diagnosis of SAP-induced ARDS were 100.0%, 81.8%, 71.4%, 100.0%, and 87.5%, respectively. CONCLUSIONS: Serum SP-A levels may allow the detection of SAP-induced ARDS and may help to support the clinical diagnosis of ARDS. The optimal serum SP-A cut-off value to discriminate SAP-induced ARDS and other groups (SO group and non-ARDS group) is around 150 ug/ml.

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. The aim of the current study is to observe the effect of CSE on biological behavior of EPCs and, further, to search for potential candidate agent in protection of proliferation of EPCs against the damage caused by CSE exposure in vitro. METHODS: The proliferations of EPCs isolated from bone marrow of C57BL/6J mice were assessed by MTT after incubating the EPCs with a series of concentrations of CSE (1.0%, 2.5%, 5.0%, and 10.0%) for different times (3, 6, and 24 hours) as well as with 1.0% CSE in presence of 5-AZA-CdR for 24 hours. RESULTS: The proliferations of EPCs were significantly enhanced after 3 hours of exposure to concentrations of 1.0% and 2.5% CSE but depressed when exposed to concentrations of 5.0% and 10.0% CSE. Furthermore, the 5-AZA-CdR in concentrations of 2.0 µmol/L and 5.0 µmol/L partly protected against the depression of proliferation of EPCs caused by CSE exposure. CONCLUSIONS: The CSE showed dual effects on proliferation of EPCs isolated from mice. The 5-AZA-CdR partly protected the proliferation of EPCs against the damage caused by CSE exposure in vitro, suggesting that DNA methylation may be involved in the dysfunction of EPCs induced by CSE.

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. Recruitment of EPCs from bone marrow to peripheral blood depends on the interaction of c-Kit/soluble c-Kit ligand (sKitL). We hypothesized that smoking might influence c-Kit(+) EPCs/sKitL interaction in bone marrow in the development of smoking-related emphysema. In this study, we used a cigarette smoke extract (CSE)-induced emphysema model. METHODS: Mice were injected intraperitoneally with PBS/CSE and sacrificed at day 28. Lung function and pathology of lung tissue were measured to characterize the model. Expressions of c-Kit in the lung tissue were assayed. Bone marrow cells were isolated by red blood cell lysis. EPCs/c-Kit(+) EPCs in nonred blood cells were analyzed by flow cytometry. Expressions of KitL and MMP-9, and activity MMP-9 in bone marrow were measured. RESULTS: Our data demonstrated that gene and protein expressions of c-Kit were decreased in the lung tissue in this model. Compared with the control group, the number of bone marrow nonred blood cells was unchanged following CSE treatment, while the depletion of bone marrow EPCs/c-Kit(+) EPCs was significant. The level of sKitL was reduced in the bone marrow in the model. The reduction of sKitL was associated with deregulated KitL expression and decreased MMP-9 activity. CONCLUSIONS: The interaction between c-Kit and sKitL in bone marrow EPCs, a critical step in endothelial repair, is negatively affected in a CSE-induced emphysema model.