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1.
Food Chem ; 336: 127730, 2021 Jan 30.
Artículo en Inglés | MEDLINE | ID: mdl-32768900

RESUMEN

Phenols are responsible for the only health claim of virgin olive oil (VOO) recognized by the European Commission EU 432/2012 and the European Food Safety Authority. In this research, we studied the decrease in the phenolic content of 160 extra VOOs (EVOOs) after 12 months storage in darkness at 20 °C. Phenolic concentration was decreased 42.0 ± 24.3% after this period and this reduction strongly depended on the initial phenolic profile. Hence, EVOOs with predominance in oleacein and oleocanthal experienced a larger decrease in phenolic content than oils enriched in other phenols. Complementarily, hydroxytyrosol and oleocanthalic acid increased significantly in aged EVOOs, which allowed their discrimination from recently produced EVOOs. These changes are explained by degradation of main secoiridoids during storage due to their antioxidant properties. Hydroxytyrosol and oleocanthalic acid can be considered markers of olive oil ageing, although they can also provide information about quality or stability.


Asunto(s)
Almacenamiento de Alimentos/métodos , Aceite de Oliva/química , Fenoles/química , Antioxidantes/química , Área Bajo la Curva , Cromatografía Líquida de Alta Presión , Iridoides/análisis , Fenoles/análisis , Alcohol Feniletílico/análogos & derivados , Alcohol Feniletílico/análisis , Curva ROC , Espectrometría de Masas en Tándem , Factores de Tiempo
2.
Gene ; 764: 145105, 2021 Jan 05.
Artículo en Inglés | MEDLINE | ID: mdl-32882333

RESUMEN

Sarcoma (SARC) represents a group of highly histological and molecular heterogeneous rare malignant tumors with poor prognosis. There are few proposed classifiers for predicting patient's outcome. The Cancer Proteome Atlas (TPCA) and The Cancer Genome Atlas (TCGA) databases provide multi-omics datasets that enable a comprehensive investigation for this disease. The proteomic expression profile of SARC patients along with the clinical information was downloaded. 55 proteins were found to be associated with overall survival (OS) of patients using univariate Cox regression analysis. We developed a prognostic risk signature that comprises seven proteins (AMPKALPHA, CHK1, S6, ARID1A, RBM15, ACETYLATUBULINLYS40, and MSH6) with robust predictive performance using multivariate Cox stepwise regression analysis. Additionally, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters. Patients in high-risk group also have worse progression free intervals (PFI) than that of patients in low-risk group, but not for disease free intervals (DFI). The signature was validated using transcriptomic profile of SARC patients from TCGA. Potential mechanisms between high- and low-risk groups were identified using differentially expressed genes (DEGs) analysis. These DEGs were primarily enriched in RAS and MPAK signaling pathways. The signature protein molecules are candidate biomarkers for SARC, and the analysis of computational biology in tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinctly immune landscapes of high- and low-risk patients. Together, we constructed a prognostic signature for predicting outcomes for SARC integrating proteomic and transcriptomic profiles, this might have value in guiding clinical practice.


Asunto(s)
Biomarcadores de Tumor/genética , Regulación Neoplásica de la Expresión Génica/inmunología , Pruebas Genéticas/métodos , Sarcoma/mortalidad , Microambiente Tumoral/inmunología , Antineoplásicos Inmunológicos/farmacología , Antineoplásicos Inmunológicos/uso terapéutico , Antígeno B7-H1/antagonistas & inhibidores , Antígeno B7-H1/genética , Antígeno B7-H1/inmunología , Antígeno CTLA-4/antagonistas & inhibidores , Antígeno CTLA-4/genética , Antígeno CTLA-4/inmunología , Conjuntos de Datos como Asunto , Femenino , Estudios de Seguimiento , Perfilación de la Expresión Génica , Humanos , Estimación de Kaplan-Meier , Linfocitos Infiltrantes de Tumor/inmunología , Linfocitos Infiltrantes de Tumor/metabolismo , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Pronóstico , Receptor de Muerte Celular Programada 1/antagonistas & inhibidores , Receptor de Muerte Celular Programada 1/genética , Receptor de Muerte Celular Programada 1/inmunología , Supervivencia sin Progresión , Mapeo de Interacción de Proteínas , Proteómica , Curva ROC , Sarcoma/tratamiento farmacológico , Sarcoma/genética , Sarcoma/inmunología , Transcriptoma/genética , Microambiente Tumoral/efectos de los fármacos , Microambiente Tumoral/genética
3.
Kardiologiia ; 60(7): 64-71, 2020 Aug 11.
Artículo en Ruso | MEDLINE | ID: mdl-33155942

RESUMEN

Aim      To analyze the relationship between serum concentrations of high-sensitivity C-reactive protein (hsCRP) in dynamics and development of restenosis at 12 months following elective coronary stent placement (CSP).Material and methods  The key role in atherogenesis, neointimal proliferation and restenosis belongs to inflammation. This study included 91 patients (median age, 60 [56; 66] years) with stable exertional angina after an elective CSP using second-generation stents. Follow-up coronarography was performed for 60 patients at 12 months. Concentration of hsCRP was measured immediately prior to CSP and at 1, 3, 6, and 12 months after CSP. Restenosis of the stented segment (50% or more narrowing of the stented segment or a 5-mm vessel segment proximally or distally adjacent to the stented segment) was observed in 8 patients.Results According to results of the ROC analysis, the increase in hsCRP concentration >0.9 mg/l (>25%) at one month after CSP had the highest predictive significance with respect of restenosis (area under the ROC curve, 0.89 at 95 % confidence interval (CI) from 0.79 to 0.99; sensitivity, 87.5 %; specificity, 82.8 %; р=0.0005), which was superior to the absolute value of hsCRP concentration >3.0 mg/l (area under the ROC curve, 0.82 at 95 % CI from 0.68 to 0.96; р=0.0007).Conclusion      Increased concentration of hsCRP ≥0.9 mg /l (≥25 %) at a month after CSP was associated with restenosis of the coronary artery stented segment.


Asunto(s)
Proteína C-Reactiva , Reestenosis Coronaria , Anciano , Angiografía Coronaria , Reestenosis Coronaria/diagnóstico , Reestenosis Coronaria/etiología , Vasos Coronarios/diagnóstico por imagen , Vasos Coronarios/cirugía , Humanos , Pronóstico , Curva ROC , Stents
4.
Zhonghua Yi Xue Za Zhi ; 100(40): 3174-3178, 2020 Nov 03.
Artículo en Chino | MEDLINE | ID: mdl-33142402

RESUMEN

Objective: To explore correlative factors and construct predictive model of intestinal flora imbalance in patients with acute exacerbation of Chronic Obstructive Pulmonary Disease (COPD). Methods: The patients in acute exacerbation stage of COPD (AECOPD) hospitalized in Yixing People's Hospital from January 1 to December 31, 2019 were included. According to the clinical symptoms and results of fecal examination, the subjects were divided into case group (n=45) and control group (n=83). Multivariate logistic regression was used to analyze the correlative factors of intestinal flora imbalance in AECOPD patients. The prediction model of intestinal flora imbalance in patients with AECOPD was constructed according to the results of factor logistic regression analysis, and the effectiveness of the prediction model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The ages of subjects in case group and control group were (76±9) and (74±8) years old, respectively, among which males accounted for 80.0% (36/45) and 69.9% (58/83), respectively. The multivariate logistic regression model analysis showed that serum albumin concentration, frequency of acute exacerbation ≥2 times/year, complicated with chronic cor pulmonale and diabetes mellitus were correlative factors of intestinal flora imbalance in patients with AECOPD. The OR (95%CI) were 0.98 (0.80-0.97), 3.70 (1.79-11.72), 2.62 (1.46-10.80) and 3.85 (1.17-8.58), respectively. The prediction model of intestinal flora imbalance was logit P=3.858-0.13×serum albumin consentration+1.52×acute exacerbation ≥2 times/year+1.379×chronic cor pulmonale+1.155×diabetes mellitus. The area under the ROC curve of this model was 0.847 and the sensitivity and specificity of the prediction model were 88.9% and 71.1%, respectively. Conclusions: Serum albumin, frequency of acute exacerbation ≥2 times/year, complicated with chronic cor pulmonale and diabetes mellitus are correlative factors of intestinal flora imbalance in patients with AECOPD. The predictive model shows high clinical value in predicting intestinal flora imbalance in patients with AECOPD.


Asunto(s)
Microbioma Gastrointestinal , Enfermedad Pulmonar Obstructiva Crónica , Anciano , Anciano de 80 o más Años , Progresión de la Enfermedad , Análisis Factorial , Humanos , Modelos Logísticos , Masculino , Curva ROC
5.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1165-1170, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198856

RESUMEN

OBJECTIVE: To evaluate the role of interleukin-6 (IL-6) and CD4+ T-lymphocytopenia in assessing the severity and prognosis of coronavirus disease 2019 (COVID-19). METHODS: A prospective observational study was conducted. Forty-five patients with COVID-19 admitted to Henan Provincial People's Hospital from January 13 to March 13, 2020 were enrolled and divided into normal group (13 cases), severe group (20 cases), critically severe group (12 cases) according to the severity of the disease. A total of 15 healthy subjects receiving physical examinations during the same period were collected as the healthy control group. Clinical data were collected to compare the clinical characteristics, general test results, IL-6 and CD4+ T-lymphocytopenia levels of patients in different disease severity groups and healthy control group. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of each indicator for the severity of COVID-19. Multivariate Cox regression analysis was used to analyze the risk factors affecting the prognosis of COVID-19 patients, and Kaplan-Meier survival curve analysis was performed. RESULTS: The age of the critically severe group was significantly higher than that of the severe and normal groups (years old: 66.91±17.01 vs. 59.35±18.07, 40.23±12.61, both P < 0.05), and the negative conversion time of the 2019 novel coronavirus (2019-nCoV) was significantly longer than that of the severe and normal groups (days: 19.00±10.66 vs. 18.00±7.18, 9.31±3.49, both P < 0.05). With the increase of the severity of disease, white blood cell count (WBC), C-reactive protein (CRP), calcitonin (PCT), total bilirubin (TBil), troponin I (TnI), IL-6, D-dimer and other indicators were significantly increased, while lymphocyte count (LYM), platelet count (PLT), CD4+, CD8+, oxygenation index (PaO2/FiO2) were significantly decreased (all P < 0.01). ROC curve showed that PaO2/FiO2, IL-6 and CD4+ had certain predictive value for disease severity of COVID-19, the area under the ROC curve (AUC) of them were 0.903, 0.871, 0.689, and the 95% confidence interval (95%CI) were 0.806-0.949, 0.769-0.974, 0.542-0.853; the best cut-off values were 196.00 mmHg (1 mmHg = 0.133 kPa), 6.02 ng/L, 355 cells/µL, respectively; the sensitivity were 73.3%, 99.3%, 73.3%, and the specificity were 96.6%, 62.1%, 65.5%, respectively. Multivariate Cox regression analysis showed that age, PaO2/FiO2, high IL-6 and low CD4+ (IL-6 ≥ 6.02 ng/L and CD4+ < 355 cells/µL) were independent risk factors affecting the prognosis of COVID-19 [hazard ratio (HR) was 1.077, 0.053 and 3.490, respectively, all P < 0.05]. Kaplan-Meier survival analysis showed that when both high IL-6 and low CD4+ (IL-6 ≥ 6.02 ng/L and CD4+ < 355 cells/µL) were present, the mean time of adverse prognosis was (20.53±5.71) days; when increased IL-6 and decreased CD4+ were inconsistent, the mean time of adverse prognosis was (53.21±3.16) days. CONCLUSIONS: The levels of IL-6 and CD4+ T-lymphocytopenia are closely related to the severity of COVID-19 disease. When IL-6 ≥ 6.02 ng/L and CD4+ < 355 cells/µL occur simultaneously, the prognosis is poor.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus , Pandemias , Neumonía Viral , Linfocitos T CD4-Positivos , Humanos , Interleucina-6 , Linfopenia , Pronóstico , Estudios Prospectivos , Curva ROC , Estudios Retrospectivos
6.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1208-1212, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198865

RESUMEN

OBJECTIVE: To establish a prognostic Nomogram model for predicting the risk of early death in polytrauma patients. METHODS: Data extracted from a polytrauma study on Dryad, an open access database, was selected for secondary analysis. Patients from 18 to 65 years old with polytrauma in the original data were included. All patients with missing variables, such as blood lactic acid (Lac), Glasgow coma score (GCS) and injury severity score (ISS) at admission, were excluded. The differences of gender, age, Lac, ISS and GCS scores between the patients who died within 72 hours and those who survived were analyzed. The risk factors for 72-hour death were analyzed by Logistic regression, and the Nomogram prediction model was established using R software. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model, and the Bootstrap method was used for internal verification by repeating sample for 1 000 times. Decision curve (DCA) was applied to analyze the clinical practical value of the model. RESULTS: A total of 2 315 polytrauma patients were included. Logistic regression analysis showed that Lac, GCS score and age > 55 years old were the risk factors for early death in polytrauma patients [Lac: odds ratio (OR) = 1.36, 95% confidence interval (95%CI) was 1.29-1.42, P < 0.001; GCS score: OR = 0.76, 95%CI was 0.73-0.79, P < 0.001; age > 55 years old: OR = 1.92, 95%CI was 1.37-2.66, P < 0.001]. The prediction model was established by using the above risk factors and displayed by Nomogram. ROC curve analysis showed that the area under the ROC curve (AUC) of Nomogram model to predict the risk of death within 72 hours was 0.858, and the predictive ability of Nomogram model was significantly higher than that of Lac (AUC = 0.743), GCS score (AUC = 0.774) and ISS score (AUC = 0.699), all P < 0.05. The model calibration chart showed that the predicting probability was consistent with the actual occurrence probability, and the DCA showed that Nomogram model presented excellent clinical value in predicting the 72-hour death risk for polytrauma patients. CONCLUSIONS: The prognostic Nomogram model presents significantly predictive value for the risk of death within 72 hours in polytrauma patients. Prognostic Nomogram model could offer individualized, visualized and graphical prediction pattern, and provide physicians with practical diagnostic tool for triage system and management of polytrauma according to precision medicine.


Asunto(s)
Traumatismo Múltiple , Nomogramas , Adolescente , Adulto , Anciano , Humanos , Persona de Mediana Edad , Pronóstico , Curva ROC , Estudios Retrospectivos , Adulto Joven
7.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1231-1235, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198870

RESUMEN

OBJECTIVE: To investigate the value of serum microRNA-92a (miR-92a) and microRNA-146a (miR-146a) expression levels combined with lung ultrasound score (LUS) in predicting the severity and prognosis of acute respiratory distress syndrome (ARDS). METHODS: 116 patients with ARDS admitted to Danzhou People's Hospital from January 2017 to March 2020 were enrolled. On the day of admission, the expression levels of serum miR-92a and miR-146a were detected by real-time fluorescent quantitative reverse transcript-polymerase chain reaction (RT-PCR), and pulmonary ultrasound examination was performed in 12 lung regions, with the total score as LUS score. The difference of each index was analyzed among the ARDS patients with different 28-day prognosis (survival group and death group) and different severity [mild group: 200 mmHg < oxygenation index (OI) ≤ 300 mmHg (1 mmHg = 0.133 kPa), moderate group: 100 mmHg < OI ≤ 200 mmHg, severe group: OI ≤ 100 mmHg]. Multivariate Logistic regression was used to analyze the risk factors of death in patients with ARDS. Receiver operating characteristic (ROC) curve was drawn to analyze the value of miR-92a and miR-146a combined with LUS score in predicting the death of patients with ARDS. RESULTS: 116 ARDS patients were included, 39 cases in the death group, 77 cases in the survival group; 20 cases in the mild group, 38 cases in the moderate group and 58 cases in the severe group. The expression levels of serum miR-92a, miR-146a and LUS score in the death group were significantly higher than those in the survival group [miR-92a (2-ΔΔCt): 3.75±1.64 vs. 2.10±0.78, miR-146a (2-ΔΔCt): 1.93±0.72 vs. 0.76±0.20,LUS score: 25.80±4.75 vs. 13.40±3.60, all P < 0.01]. With the aggravation of ARDS patients, the expression levels of serum miR-92a and miR-146a and LUS score gradually increased (F values were 8.115, 6.740 and 6.216 respectively, all P < 0.01). The expression levels of serum miR-92a, miR-146a and LUS score in severe group were significantly higher than those in the moderate group and mild group [miR-92a (2-ΔΔCt): 3.65±1.62 vs. 2.87±1.16, 1.94±0.68; miR-146a (2-ΔΔCt): 1.85±0.58 vs. 1.30±0.51, 0.68±0.17; LUS score: 24.15±4.65 vs. 18.60±4.20, 12.20±3.15, all P < 0.01]. Multivariate Logistic regression analysis showed that low OI [odds ratio (OR) = 2.748, 95% confidence interval (95%CI) was 1.913-6.225, P = 0.024], high LUS score (OR = 1.685, 95%CI was 1.183-2.758, P = 0.016), high expression levels of serum miR-92a (OR = 2.560, 95%CI was 1.806-5.627, P < 0.001) and miR-146a (OR = 1.984, 95%CI was 1.375-3.816, P = 0.008) were independent risk factors for the death of ARDS patients. ROC curve analysis showed that the area under ROC curve (AUC) of patients with ARDS predicted by miR-92a and miR-146a combined with LUS score was significantly higher than that predicted by the three alone (0.918 vs. 0.842, 0.825, 0.807, all P < 0.01), and the sensitivity (94.0%) and specificity (85.2%) were higher. CONCLUSIONS: The expression levels of serum miR-92a, miR-146a and LUS score are related to the severity and prognosis of the patients with ARDS, and the combination of the three indicators has better value in predicting the prognosis of the patients with ARDS.


Asunto(s)
MicroARNs/genética , Síndrome de Dificultad Respiratoria del Adulto , Humanos , Pulmón/diagnóstico por imagen , Pronóstico , Curva ROC , Síndrome de Dificultad Respiratoria del Adulto/diagnóstico por imagen , Síndrome de Dificultad Respiratoria del Adulto/genética , Ultrasonografía
8.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1236-1240, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198871

RESUMEN

OBJECTIVE: To evaluate the clinical characteristics, diagnosis, treatment and outcome of elderly patients with acute pulmonary embolism (APE), in order to strengthen the awareness of diagnosis of APE and reduce missed diagnosis and misdiagnosis. METHODS: A retrospective analysis of clinical data of 40 elderly patients (age ≥ 60 years old) diagnosed with APE admitted to TEDA International Cardiovascular Hospital from January 2008 to December 2018, including risk factors, clinical features, symptoms and signs, laboratory tests, risk of pulmonary embolism (Wells score), simplified pulmonary embolism severity index (sPESI), radiological tests, treatment, and outcome, etc. were conducted. Receiver operating characteristic (ROC) curve was drawn to analyze the diagnostic value of Wells score and spiral CT pulmonary angiography (CTPA) in APE. RESULTS: A total of 40 elderly patients with APE were enrolled, male was 52.5%, and the age was (69.6±8.2) years old. The most common risk factor was deep vein thrombosis (DVT, 52.5%), followed by hypertension (37.5%) and heart failure (35.0%). The main clinical symptoms were exertional dyspnea (87.5%) and chest tightness (80.0%). Only 10.0% of patients had the triad of dyspnea, chest pain and hemoptysis at the same time. In addition, palpitation (65.0%) and lower limb swelling and pain (42.5%) were also common symptoms. The main clinical signs were shortness of breath (respiratory rate > 25 bpm, 80.0%), lung moist rales (52.5%), and tachycardia (heart rate > 100 bpm, 50.0%). The Wells score showed that 95% of the patients Wells ≥ 2, including moderate (Wells 2-6, 62.5%) and severe (Wells ≥ 7, 32.5%). Laboratory examination showed that 80.0% of patients had D-dimer > 0.5 mg/L, 72.5% had arterial partial pressure of oxygen (PaO2) < 60 mmHg (1 mmHg = 0.133 kPa), and 75.0% had arterial partial pressure of carbon dioxide (PaCO2) < 35 mmHg, 67.5% had brain natriuretic peptide (BNP) > 500 ng/L or N-terminal pro-BNP (NT-proBNP) > 300 ng/L, and 47.5% had cardiac troponin I (cTnI) > 0.3 µg/L. The confirmed diagnosis rate of CTPA in APE was 88.6% (31/35); 5 cases were diagnosed by pulmonary ventilation/perfusion imaging in 6 cases; 4 cases were diagnosed by magnetic resonance pulmonary angiography (MRPA). The sPESI score showed that 36 patients were moderate-risk patients [26 patients with sPESI ≥ 1, and 10 patients with sPESI 0 but right ventricular dysfunction (RVD) and/or elevated cardiac biomarkers]. Thrombolytic therapy and anticoagulant therapy were performed on 17 of them: 8 were cured, 8 were improved, and 1 died; anticoagulant therapy was performed on 18 moderate-risk patients: 9 were cured, 7 were improved, 1 left the hospital without cure, and 1 died; the other 1 moderate-risk patient with PE caused by right atrial myxoma was treated by operation and ultimately died. Four low-risk patients were treated by anticoagulant therapy: 2 were cured and 2 improved. The area under the ROC curve (AUC) of Wells score combined with CTPA was 0.82 (95% confidence interval was 0.73-0.98, P < 0.01), the sensitivity was 74.2%, and the specificity was 90.0%. CONCLUSIONS: DVT and chronic diseases are the most common risk factors for APE in the elderly patients, often accompanied by dyspnea, chest tightness, and lower limb swelling and pain. Early anticoagulation therapy in elderly APE can make a good prognosis. Wells score has an important predictive value for the diagnosis of APE, while blood D-dimer is an important exclusion parameter. CTPA test is the main diagnostic method for APE. The sPESI score can suggest risk stratification and prognosis, and further guided treatment.


Asunto(s)
Embolia Pulmonar , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Embolia Pulmonar/diagnóstico por imagen , Curva ROC , Estudios Retrospectivos , Troponina I , Disfunción Ventricular Derecha
9.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1253-1256, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198875

RESUMEN

OBJECTIVE: To explore the clinical characteristics and prognostic risk factors of severe influenza. METHODS: Clinical data of severe influenza patients admitted to the department of respiratory and critical care medicine of the Second Affiliated Hospital of Anhui Medical University from March 2014 to June 2019 were retrospectively analyzed. General information, laboratory test results, and etiological test results of the hospitalization outcomes for survival group and death group during the 28-day follow-up were analyzed using Logistic regression analysis. RESULTS: Among the 37 patients, 29 were males and 8 were females. They aged 25-86 years old with an average of (59.59±15.16) years old. Twenty-one cases had chronic underlying diseases; 28 cases had co-infections, including 6 cases with bacterial infections, 7 cases with fungal infections, 3 case with other pathogens, and 12 cases with mixed infection. Among the 37 patients, 9 died during hospitalization and 5 died within 28-day of discharge. The overall mortality rate was 37.84%. Compared with the survival group, patients in the death group were older (years old: 66.57±3.94 vs. 55.35±14.53), British Thoracic Society's modified pneumonia score (CURB-65 score), acute physiology and chronic health evaluation II (APACHE II) score, neutrophil count, D-dimer, 48-hour C-reactive protein (CRP) and procalcitonin (PCT) were higher [CURB-65 score: 2 (2, 3) vs. 1 (0, 2), APACHE II: 16.00±4.62 vs. 11.00±4.22, neutrophil count (×109/L): 8.87 (5.42, 11.33) vs. 3.58 (2.55, 7.13), D-dimer (mg/L): 7.97 (5.19, 12.68) vs. 2.91 (1.19, 5.02), 48-hour CRP (mg/L): 127.83±92.24 vs. 87.01±57.00, 48-hour PCT (µg/L): 1.79 (0.59, 4.44) vs. 0.37 (0.13, 0.99)], oxygenation index (PaO2/FiO2) and creatinine clearance rate were lower [PaO2/FiO2 (mmHg, 1 mmHg = 0.133 kPa): 109.52±49.30 vs. 204.82±67.61, creatinine clearance rate (mL×min-1×1.73 m-2): 55.49±21.23 vs. 77.59±29.73], and the differences were statistically significant (all P < 0.05). There was no significant difference in gender, combined chronic underlying diseases, lymphocyte count, albumin, lactate dehydrogenase (LDH), prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (Fib), 24-hour CRP and PCT between the two groups. A total of 37 pathogens were cultured, including 17 Gram-negative bacteria (45.95%), 3 Gram-positive bacteria (8.10%), and 17 fungi (45.95%). The number of Acinetobacter baumannii infections in the death group was significantly higher than that in the survival group (cases: 7 vs. 2, P < 0.05). Logistic regression analysis showed that age, CURB-65 score, APACHE II score, PaO2/FiO2, neutrophil count, creatinine clearance rate, combined Acinetobacter baumannii infection, deep vein catheterization, catheterization, and stomach preservation during hospitalization were risk factors for the prognosis of patients with severe influenza [hazard ratios (HR) were 1.064, 4.920, 1.286, 0.975, 1.286, 0.965, 0.095, 0.083, 9.333, 0.089, respectively, all P < 0.05]. Multivariate analysis showed that low PaO2/FiO2 and Acinetobacter baumannii infection were risk factors for prognosis of severe influenza (HR were 0.834 and 0.000, respectively, both P < 0.05). CONCLUSIONS: Old age, high CURB-65 score, high APACHE II score, and co-infection are risk factors for the prognosis of patients with severe influenza.


Asunto(s)
Gripe Humana , Neumonía , APACHE , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Gripe Humana/diagnóstico , Masculino , Persona de Mediana Edad , Pronóstico , Curva ROC , Estudios Retrospectivos
10.
PLoS One ; 15(11): e0241825, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33175863

RESUMEN

BACKGROUND: Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. METHODS AND FINDINGS: We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality-no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77-0.81), and in early (AUC = 0.81 95% CI: 0.78-0.83) and late (AUC = 0.84, 95% CI: 0.78-0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60-64 years, overall mortality was estimated at 9% (95% CI: 6-11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3-7%) to 21% (95% CI: 12-31%), depending on sex and comorbid disease. CONCLUSION: Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.


Asunto(s)
Infecciones por Coronavirus/mortalidad , Neumonía Viral/mortalidad , Adulto , Factores de Edad , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Betacoronavirus/aislamiento & purificación , Comorbilidad , Infecciones por Coronavirus/patología , Infecciones por Coronavirus/virología , Bases de Datos Factuales , Grupos Étnicos , Femenino , Humanos , Modelos Logísticos , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/patología , Neumonía Viral/virología , Curva ROC , Factores de Riesgo , Salud de los Veteranos , Adulto Joven
11.
PLoS One ; 15(11): e0237828, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33137138

RESUMEN

There is an urgent need for an accurate antibody test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have developed 3 ELISA methods, trimer spike IgA, trimer spike IgG, and nucleocapsid IgG, for detecting anti-SARS-CoV-2 antibodies. We evaluated their performance along with four commercial ELISAs, EDI™ Novel Coronavirus COVID-19 ELISA IgG and IgM, Euroimmun Anti-SARS-CoV-2 ELISA IgG and IgA, and one lateral flow assay, DPP® COVID-19 IgM/IgG System (Chembio). Both sensitivity and specificity were evaluated and the probable causes of false-positive reactions were determined. The assays were evaluated using 300 pre-epidemic samples and 100 PCR-confirmed COVID-19 samples. The sensitivities and specificities of the assays were as follows: 90%/100% (in-house trimer spike IgA), 90%/99.3% (in-house trimer spike IgG), 89%/98.3% (in-house nucleocapsid IgG), 73.7%/100% (EDI nucleocapsid IgM), 84.5%/95.1% (EDI nucleocapsid IgG), 95%/93.7% (Euroimmun S1 IgA), 82.8%/99.7% (Euroimmun S1 IgG), 82.0%/91.7% (Chembio nucleocapsid IgM), 92%/93.3% (Chembio nucleocapsid IgG). The presumed causes of false positive results from pre-epidemic samples in commercial and in-house assays were mixed. In some cases, assays lacked reproducibility. In other cases, reactivity was abrogated by competitive inhibition (spiking the sample with the same antigen that was used for coating ELISAs prior to performing the assay), suggesting positive reaction could be attributed to the presence of antibodies against these antigens. In other cases, reactivity was consistently detected but not abrogated by the spiking, suggesting positive reaction was not attributed to the presence of antibodies against these antigens. Overall, there was wide variability in assay performance using our samples, with in-house tests exhibiting the highest combined sensitivity and specificity. The causes of "false positivity" in pre-epidemic samples may be due to plasma antibodies apparently reacting with the corresponding antigen, or spurious reactivity may be directed against non-specific components in the assay system. Identification of these targets will be essential to improving assay performance.


Asunto(s)
Anticuerpos Antivirales/sangre , Betacoronavirus/metabolismo , Infecciones por Coronavirus/diagnóstico , Inmunoensayo/métodos , Nucleocápside/inmunología , Neumonía Viral/diagnóstico , Glicoproteína de la Espiga del Coronavirus/inmunología , Adulto , Anciano , Anciano de 80 o más Años , Área Bajo la Curva , Betacoronavirus/aislamiento & purificación , Infecciones por Coronavirus/virología , Femenino , Humanos , Inmunoglobulina A/sangre , Inmunoglobulina G/sangre , Inmunoglobulina M/sangre , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/virología , Curva ROC , Reproducibilidad de los Resultados
12.
PLoS One ; 15(11): e0241262, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33137167

RESUMEN

The coronavirus disease 2019 (COVID-19) has become a pandemic. Rapidly distinguishing COVID-19 from other respiratory infections is a challenge for first-line health care providers. This retrospective study was conducted at the Taipei Medical University Hospital, Taiwan. Patients who visited the outdoor epidemic prevention screening station for respiratory infection from February 19 to April 30, 2020, were evaluated for blood biomarkers to distinguish COVID-19 from other respiratory infections. Monocyte distribution width (MDW) ≥ 20 (odds ratio [OR]: 8.39, p = 0.0110, area under curve [AUC]: 0.703) and neutrophil-to-lymphocyte ratio (NLR) < 3.2 (OR: 4.23, p = 0.0494, AUC: 0.673) could independently distinguish COVID-19 from common upper respiratory tract infections (URIs). Combining MDW ≥ 20 and NLR < 3.2 was more efficient in identifying COVID-19 (AUC: 0.840). Moreover, MDW ≥ 20 and NLR > 5 effectively identified influenza infection (AUC: 0.7055). Thus, MDW and NLR can distinguish COVID-19 from influenza and URIs.


Asunto(s)
Infecciones por Coronavirus/patología , Gripe Humana/patología , Linfocitos/citología , Monocitos/citología , Neutrófilos/citología , Neumonía Viral/patología , Área Bajo la Curva , Biomarcadores/metabolismo , Infecciones por Coronavirus/inmunología , Femenino , Humanos , Gripe Humana/inmunología , Linfocitos/metabolismo , Masculino , Monocitos/metabolismo , Neutrófilos/metabolismo , Oportunidad Relativa , Pandemias , Proyectos Piloto , Neumonía Viral/inmunología , Curva ROC , Infecciones del Sistema Respiratorio/inmunología , Infecciones del Sistema Respiratorio/patología
13.
Nat Commun ; 11(1): 5668, 2020 11 09.
Artículo en Inglés | MEDLINE | ID: mdl-33168827

RESUMEN

Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.


Asunto(s)
Lesión Renal Aguda/etiología , Inteligencia Artificial , Aprendizaje Automático , Lesión Renal Aguda/sangre , Adolescente , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Curva ROC , Medición de Riesgo , Factores de Riesgo , Adulto Joven
14.
BMC Bioinformatics ; 21(1): 507, 2020 Nov 07.
Artículo en Inglés | MEDLINE | ID: mdl-33160328

RESUMEN

BACKGROUND: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory when training and testing data from different cell lines. Currently, it is still unclear what extent a across cell line prediction can be made based on sequence-level information. RESULTS: In this work, we present a novel Sequence-based method (called SEPT) to predict the enhancer-promoter interactions in new cell line by using the cross-cell information and Transfer learning. SEPT first learns the features of enhancer and promoter from DNA sequences with convolutional neural network (CNN), then designing the gradient reversal layer of transfer learning to reduce the cell line specific features meanwhile retaining the features associated with EPIs. When the locations of enhancers and promoters are provided in new cell line, SEPT can successfully recognize EPIs in this new cell line based on labeled data of other cell lines. The experiment results show that SEPT can effectively learn the latent import EPIs-related features between cell lines and achieves the best prediction performance in terms of AUC (the area under the receiver operating curves). CONCLUSIONS: SEPT is an effective method for predicting the EPIs in new cell line. Domain adversarial architecture of transfer learning used in SEPT can learn the latent EPIs shared features among cell lines from all other existing labeled data. It can be expected that SEPT will be of interest to researchers concerned with biological interaction prediction.


Asunto(s)
Redes Neurales de la Computación , Regiones Promotoras Genéticas/genética , Secuencias Reguladoras de Ácidos Nucleicos/genética , Área Bajo la Curva , Línea Celular , Humanos , Curva ROC
15.
BMC Bioinformatics ; 21(1): 442, 2020 Oct 07.
Artículo en Inglés | MEDLINE | ID: mdl-33028186

RESUMEN

BACKGROUND: Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein-protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. RESULTS: According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. CONCLUSION: Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.


Asunto(s)
Mapeo de Interacción de Proteínas/métodos , Mapas de Interacción de Proteínas , Animales , Área Bajo la Curva , Bases de Datos de Proteínas , Redes Reguladoras de Genes , Ratones , Fenotipo , Curva ROC , Interfaz Usuario-Computador , Pez Cebra/metabolismo
16.
BMC Bioinformatics ; 21(1): 444, 2020 Oct 07.
Artículo en Inglés | MEDLINE | ID: mdl-33028191

RESUMEN

BACKGROUND: Metabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users. RESULTS: Integrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC-MS and LC-MS respectively. CONCLUSION: IP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.


Asunto(s)
Metaboloma , Metabolómica/métodos , Interfaz Usuario-Computador , Área Bajo la Curva , Cromatografía Líquida de Alta Presión , Análisis por Conglomerados , Minería de Datos , Cromatografía de Gases y Espectrometría de Masas , Humanos , Espectrometría de Masas , Curva ROC
17.
Medicine (Baltimore) ; 99(42): e22676, 2020 Oct 16.
Artículo en Inglés | MEDLINE | ID: mdl-33080712

RESUMEN

The purpose of this study is to present a new day 4 (D4) embryo grading system for the assessment of embryos in frozen-thawed embryo transfer (FET) cycles.A new grading system (grades A-E) was modified from the 2011 ESHRE Istanbul Consensus for D4 embryos in FET cycles. In total, we retrospectively analyzed 5640 embryos with known implantation data after D4 transfer in FET cycles by using this proposed grading model.The transferred embryos exhibited a similar declining trend in implantation rates from the top grade A to the lowest grade E. The implantation rates of grade B and E embryos in the in vitro fertilization group were significantly higher than that in the intracytoplasmic sperm injection group (grade B: 41.82%, 35.23%, χ = 5.85, P < .05 and grade E: 18.53%, 14.81, χ = 76.86, P < .01, respectively). The receiver operating characteristic analysis showed that our proposed model predicted the implantation outcomes of all embryos (area under the ROC curve = 0.65; 95% CI, 0.63-0.66; P < .01).This study demonstrated that the new grading system provided by us turned out to be a useful tool in assisting embryo selection via embryo morphological changes, and D4 embryo transfer provided a simple and applicable method for a daily routine in FET cycles.


Asunto(s)
Implantación del Embrión , Transferencia de Embrión , Adulto , Estudios de Cohortes , Criopreservación , Femenino , Humanos , Registros Médicos , Embarazo , Resultado del Embarazo , Curva ROC , Estudios Retrospectivos
18.
BMC Bioinformatics ; 21(1): 457, 2020 Oct 15.
Artículo en Inglés | MEDLINE | ID: mdl-33059594

RESUMEN

BACKGROUND: The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data. RESULTS: Here, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine regression. CONCLUSION: We found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.


Asunto(s)
Algoritmos , Asma/genética , Perfilación de la Expresión Génica , Regulación de la Expresión Génica , Esputo/metabolismo , Área Bajo la Curva , Asma/patología , Análisis por Conglomerados , Humanos , Anotación de Secuencia Molecular , Curva ROC , Índice de Severidad de la Enfermedad , Máquina de Vectores de Soporte
19.
Geriatr Gerontol Int ; 20(9): 811-816, 2020 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-33058420

RESUMEN

AIM: The Geriatric Nutritional Risk Index (GNRI) can predict nutritional risk. However, just a few studies have validated the optimal cut-off value of GNRI for nutrition screening in older patients. Hence, this study aimed to determine the optimal value of GNRI to screen the risk of malnutrition among older patients. METHODS: This retrospective cross-sectional study was carried out with 5867 consecutive older adult patients who were admitted to an academic hospital in Japan. Receiver operating characteristic curve analyses were carried out to obtain the optimal cut-off value of GNRI, and the results were compared against the Mini Nutritional Assessment - Short Form and Malnutrition Universal Screening Tool. The validation of the obtained cut-off value was examined on the concordance rate of malnutrition diagnosis based on the European Society of Clinical Nutrition and Metabolism criteria. RESULTS: The mean age of the patients was 76.0 ± 7.0 years. The optimal cut-off value of GNRI for Mini Nutritional Assessment - Short Form ≤11 points was 95.92 (area under the curve 0.827 [0.817-0.838], P < 0.001), and that for Malnutrition Universal Screening Tool ≥1 point was 95.95 (area under the curve 0.788 [0.776-0.799], P < 0.001). By adapting GNRI <96 points as an initial screening cut-off in the European Society of Clinical Nutrition and Metabolism-defined malnutrition process, the concordance rates of comparisons were 98.5% and 98.5% for Mini Nutritional Assessment - Short Form-based and MUST-based diagnosis, respectively. CONCLUSIONS: The study showed GNRI <96 points as the optimal cut-off value for nutritional screening. GNRI might be one of the easy-to-use tools for nutritional screening and for diagnosing malnutrition in older adults. Geriatr Gerontol Int 2020; 20: 811-816.


Asunto(s)
Desnutrición/diagnóstico , Tamizaje Masivo/métodos , Evaluación Nutricional , Estado Nutricional/fisiología , Anciano , Anciano de 80 o más Años , Antropometría , Estudios Transversales , Femenino , Evaluación Geriátrica/métodos , Hospitalización , Hospitales Universitarios , Humanos , Japón , Masculino , Curva ROC , Estudios Retrospectivos
20.
Medicine (Baltimore) ; 99(42): e22433, 2020 Oct 16.
Artículo en Inglés | MEDLINE | ID: mdl-33080676

RESUMEN

The chest computed tomography (CT) characteristics of coronavirus disease 2019 (COVID-19) are important for diagnostic and prognostic purposes. The aim of this study was to investigate chest CT findings in COVID-19 patients in order to determine the optimal cut-off value of a CT severity score that can be considered a potential prognostic indicator of a severe/critical outcome.The CT findings were evaluated by means of a severity score that included the extent (0-4 grading scale) and nature (0-4 grading scale) of CT abnormalities. The images were evaluated at 3 levels bilaterally. A receiver operating characteristics (ROC) curve was used to identify the optimal score (Youden's index) predicting severe/critical COVID-19.The study involved 165 COVID-19 patients (131 men [79.4%] and 34 women [20.6%] with a mean age of 61.5 ±â€Š12.5 years), of whom 30 (18.2%) had severe/critical disease and 135 (81.8%) mild/typical disease. The most frequent CT finding was bilateral predominantly subpleural and basilar airspace changes, with more extensive ground-glass opacities than consolidation. CT findings of consolidation, a crazy-paving pattern, linear opacities, air bronchogram, and extrapulmonary lesions correlated with severe/critical COVID-19. The mean CT severity score was 63.95 in the severe/critical group, and 35.62 in the mild/typical group (P < .001). ROC curve analysis showed that a CT severity score of 38 predicted the development of severe/critical symptoms.A CT severity score can help the risk stratification of COVID-19 patients.


Asunto(s)
Infecciones por Coronavirus/diagnóstico por imagen , Infecciones por Coronavirus/patología , Neumonía Viral/diagnóstico por imagen , Neumonía Viral/patología , Tomografía Computarizada por Rayos X/normas , Adulto , Anciano , Betacoronavirus , Femenino , Humanos , Pulmón/diagnóstico por imagen , Pulmón/patología , Masculino , Persona de Mediana Edad , Oxígeno/sangre , Pandemias , Pronóstico , Curva ROC , Frecuencia Respiratoria , Tomografía Computarizada por Rayos X/métodos
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