Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Serum Proteomic Changes as Candidate Biomarkers of Intermediate Liver Fibrosis in Chronic Hepatitis B Infection.

Chronic hepatitis B (CHB) is a major global health burden. Liver fibrosis, an insidious process, is the main histopathological change in CHB that might lead to the end-stage liver disease if left untreated. The intermediate liver fibrosis (S2) is the optimal time to start antiviral therapy. The aim of the present study was to examine the proteomic changes in patients with CHB at different fibrotic stages, with a view to identify future serum biomarkers for S2. Ninety CHB patients were grouped into mild (S0-1), intermediate (S2), and severe liver fibrosis (S3-4) (61 men and 29 women; age 25-63 years). Isobaric tagging for relative and absolute quantitation was applied to screen proteins differentially expressed among the patient groups. Another 46 patients with CHB (age 25-59 years; 31 men and 15 women), and 16 healthy controls (age 26-61 years; 11 men and 5 women) were enrolled in a validation group. Enzyme-linked immunosorbent assay was used to verify the diagnostic value of the candidate biomarkers. We found 139 proteins that were differentially expressed between various fibrotic stage-paired comparisons. Five protein candidates were selected as potential biomarkers of S2 for further verification. Notably, ficolin-2 (FCN2) and carboxypeptidase B2 (CPB2) showed differential expression between patients and healthy controls. In conclusion, serum proteomic changes reported here offer new molecular leads for future research on biomarker candidates to identify liver fibrotic stages in CHB. In particular, FCN2 and CPB2 warrant further research on their possible mechanistic involvement in CHB pathogenesis.

Identification of potential metabolic biomarkers of cerebrospinal fluids that differentiate tuberculous meningitis from other types of meningitis by a metabolomics study.

Tuberculous meningitis (TBM) is caused by tuberculosis infection of of the meninges, which are the membrane systems that encircle the brain, with a high morbidity and mortality rate. It is challenging to diagnose TBM among other types of meningitis, such as viral meningitis, bacterial meningitis and cryptococcal meningitis. We aimed to identify metabolites that are differentially expressed between TBM and the other types of meningitis by a global metabolomics analysis. The cerebrospinal fluids (CSF) from 50 patients with TBM, 17 with viral meningitis, 17 with bacterial meningitis, and 16 with cryptococcal meningitis were analyzed using ultra high performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS). A total of 1161 and 512 features were determined in positive and negative electrospray ionization mode, respectively. A clear separation between TBM and viral, bacterial or cryptococcal meningitis was achieved by orthogonal projections to latent structures-discriminate analysis (OPLS-DA) analysis. Potential metabolic markers and related pathways were identified, which were mainly involved in the metabolism of amino acid, lipids and nucleosides. In summary, differential metabolic profiles of the CSF exist between TBM and other types of meningitis, and potential metabolic biomarkers were identified to differentiate TBM from other types of meningitis.

Old age and hydrocephalus are associated with poor prognosis in patients with tuberculous meningitis: A retrospective study in a Chinese adult population.

Tuberculous meningitis (TBM) is the most common form of central nervous system tuberculosis with a very poor prognosis. We aimed at assessing risk factors related to the prognosis of patients with TBM.Forty-five inpatients with TBM in our institution from January 2013 to December 2015 were enrolled retrospectively. The good or poor prognosis in the patients was defined, based on Glasgow Outcome Scale System at discharge. Patients with a GOS score less than 5 were defined as "poor prognosis." Univariate and multivariate logistic regression analyses were performed to assess the predictors for TBM outcome.Among 45 TBM patients, 35 (77.8%) and 10 (22.2%) were in good, poor prognoses, respectively. Old age, disturbance of consciousness, moderate to severe electroencephalogram abnormality, hydrocephalus, remarkable increase of protein (≥ 236 mg/dL) and white blood cell counts (≥ 243 /µL) in cerebral spinal fluid were associated with poor prognosis. Multivariate analysis indicated that old age (odds ratio (OR) = 18.395, P = .036) and hydrocephalus (OR = 32.995, P = .049) were independent factors for a poor outcome of TBM.In conclusion, old age and hydrocephalus are the predictors for poor prognosis of TBM. Patients with these risk factors should be treated promptly with a special care paid to improve their outcomes.

Peliosis hepatis: 2 case reports of a rare liver disorder and its differential diagnosis.

RATIONALE: Peliosis hepatis (PH) is a rare tumor-like liver lesion composed of multiple blood-filled cavities within the liver parenchyma. It is hard to differentiate PH from other liver lesions by imaging, such as carcinoma, metastases, or abscess. PATIENT CONCERNS: Here, we reported 2 cases that presented with liver lesions under ultrasound and computed tomography (CT) scanning, without any history of liver diseases or drug usage traced back. DIAGNOSES: Liver biopsy and laparoscopy were processed, and the lesions were eventually diagnosed as PH by histopathology, which microscopically presented with multiple sinusoidal dilatations with blood-filled cystic spaces. INTERVENTIONS: After the liver biopsy or laparoscopy, the patients were discharged and followed up in the clinic. OUTCOMES: Both patients were followed up for at least 1 year with good recovery. LESSONS: PH should always be recognized in the differentiation of liver lesions, particularly indistinctive lesion(s) without any history of liver-related diseases.

[Clinical significance of serum differential protein examination in chronic hepatitis B related liver fibrosis].

OBJECTIVE: To investigate the clinical significance of the expression of serum differential protein in patients with chronic hepatitis B (CHB) related liver fibrosis. METHODS: One hundred and ten CHB patients confirmed by liver biopsies were enrolled, 83 for modeling and 27 for verification. According to Ishak staging, 55 patients in the modeling group were with significant liver fibrosis ( F is more than or equal to 3 ) and 28 patients with normal/mild liver fibrosis ( F0-F2 ). While that in the verification group were 15 ( F is more than or equal to 3 ) and 12 ( F0-F2 ), respectively. MALDI-TOF-MS/MS was used to detect serum proteins and the spectrum for each sample was analyzed in FlexAnalysis3.0 to produce the spectrum of differential proteins. The results were compared with clinicopathologic diagnosis and the diagnosis model based on genetic algorithm was established and evaluated. RESULTS: There were 15 proteins differentially expressed in significant liver fibrosis group and normal/mild fibrosis group ( P value is less than 0.01), in which the differences on proteins 2081.73 m/z and 1944.41 m/z were the most significant. Based on these two proteins, the coordinate system was set up and the diagnosis model based on genetic algorithm was established by six characteristic peaks. After detecting 12 cases of normal/mild liver fibrosis and 15 cases of significant liver fibrosis, the results showed that the diagnostic model could identify significant fibrosis ( F is more than or equal to 3 ) and normal/mild liver fibrosis ( F0-F2 ) at 100% recognition, 94.14% prediction and 100% accuracy. CONCLUSION: Serum differential proteins examination can be used for early prediction of CHB related fibrosis. The study provides the basis for non-invasive diagnosis of hepatic fibrosis according to identifying the potential differences of the serum samples from patients with HBV related fibrosis.