Construction and Validation of a Nomogram Model to Predict the Severity of Mycoplasma pneumoniae Pneumonia in Children.

Background: This study aimed to develop a nomogram model for early prediction of the severe Mycoplasma pneumoniae pneumonia (MPP) in children. Methods: A retrospective analysis was conducted on children with MPP, classifying them into severe and general MPP groups. The risk factors for severe MPP were identified using Logistic Stepwise Regression Analysis, followed by Multivariate Regression Analysis to construct the nomogram model. The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Results: Univariate analysis revealed that age, duration of fever, length of hospital-stay, decreased sounds of breathing, respiratory rate, hypokalemia, and incidence of co-infection were significantly different between severe and general MPP. Significant differences (p < 0.05) were also observed in C-reactive protein, procalcitonin, peripheral blood lymphocyte count, neutrophil-to-lymphocyte ratio, ferritin, lactate dehydrogenase, alanine aminotransferase, interleukin-6, immunoglobulin A, and CD4+ T cells between the two groups. Logistic Stepwise Regression Analysis showed that age, decreased sounds of breathing, respiratory rate, duration of fever (OR = 1.131; 95% CI: 1.060-1.207), length of hospital-stay (OR = 1.415; 95% CI: 1.287-1.555), incidence of co-infection (OR = 1.480; 95% CI: 1.001-2.189), ferritin level (OR = 1.003; 95% CI: 1.001-1.006), and LDH level (OR = 1.003; 95% CI: 1.001-1.005) were identified as risk factors for the development of severe MPP (p < 0.05 in all). The above factors were applied in constructing a nomogram model that was subsequently tested with 0.862 of the area under the ROC curve. Conclusion: Age, decreased sound of breathing, respiratory rate, duration of fever, length of hospital-stay, co-infection with other pathogen(s), ferritin level, and LDH level were the significant contributors for the establishment of a nomogram model to predict the severity of MPP in children.

Invasive pulmonary and central nervous system aspergillosis in a child: A case report and literature review.

RATIONALE: Children with haematological malignancies have a higher risk of developing aggressive pulmonary aspergillosis and a higher mortality rate. The most common site of extrapulmonary aspergillosis in children is the central nervous system (CNS), and the death rate is higher when CNS is affected. Therefore, early diagnosis and treatment of invasive aspergillosis are essential for reducing mortality. PATIENT CONCERNS: We report a case of an 8-year-old girl with acute lymphoblastic leukaemia who developed invasive pulmonary aspergillosis complicated by CNS aspergillosis. Aspergillus was confirmed by metagenomic sequencing of pathogenic microorganisms. DIAGNOSES: Invasive pulmonary and central nervous system aspergillosis. INTERVENTIONS: The patient was treated with combined systemic antifungal agents (voriconazole and liposomal amphotericin B) and intrathecal injection of amphotericin B. OUTCOMES: The treatment was well tolerated and resulted in remarkable clinical and radiological head improvements. LESSONS: Invasive aspergillosis has a high mortality rate and requires early diagnosis and treatment. Pathogenic microbial metagenomic sequencing is a convenient method to assist in the early diagnosis of aspergillosis. Voriconazole is the drug of choice for the treatment of invasive aspergillosis. When CNS aspergillosis occurs, it can be combined with other systemic antifungal drugs and intrathecal injection of amphotericin B.

Improving Biomedical Question Answering by Data Augmentation and Model Weighting.

Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it's more difficult to build large-scale datasets for specific domain question answering. Existing methods are limited by the lack of training data, and the performance is not as good as in open-domain settings, especially degrading when facing to the adversarial sample. We try to resolve the above issues. First, effective data augmentation strategies are adopted to improve the model training, including slide window, summarization and round-trip translation. Second, we propose a model weighting strategy for the final answer prediction in biomedical domain, which combines the advantage of two models, open-domain model QANet and BioBERT pre-trained in biomedical domain data. Finally, we give adversarial training to reinforce the robustness of the model. The public biomedical dataset collected from PubMed provided by BioASQ challenge is used to evaluate our approach. The results show that the model performance has been improved significantly compared to the single model and other models participated in BioASQ challenge. It can learn richer semantic expression from data augmentation and adversarial samples, which is beneficial to solve more complex question answering problems in biomedical domain.

Phenotypes of the inflammatory cells in the induced sputum from young children or infants with recurrent wheezing.

BACKGROUND: To identify inflammatory cell types by phenotypic analysis of the inflammatory cells in the induced sputum. METHODS: This retrospective study included 1232 children and infants, who were assigned into mild/moderate groups (326) and severe group (602) by clinical symptom scores. Phenotypes of sputum inflammatory cells were analyzed using liquid-based thin-cytologic test and eosinophil-derived neurotoxin (EDN) was quantified by ELISA. RESULTS: Blood eosinophil count, serum total IgE level, and allergen detection rate were significantly higher in the severe group. In the 905 cases of qualified sputum, 526 cases exhibited at least one type of inflammatory cells, including neutrophil (343, 65.2%), eosinophil (161, 30.6%), and mixed granulocytes (22, 4.2%). Levels of neutrophils and eosinophils were significantly higher in the severe group than mild/moderate group, and eosinophil was predominant in the severe group. Serum EDN was 104.8 ± 39.4 µg/l in the eosinophil phenotype group, 112.6 ± 41.2 µg/l in the mixed group, 88.2 ± 36.6 µg/l in the neutrophil phenotype group, and 60.9 ± 34.6 µg/l in the paucigranulocytic phenotype group. CONCLUSION: Induced-sputum inflammatory cell count may be used to determine phenotype of wheezing. The criteria of classifying adult asthma could be applicable for children and infants.

Analysis of the predicting factors of recurrent wheezing in infants.

BACKGROUND: Clinically, asthma in children under 5 years old is under estimated because lack of diagnostic criteria. The current study was, therefore, designed to identify the predicting factors for recurrent wheezing in infants. METHODS: One hundred forty-five infants under 3-year old hospitalized with respiratory diseases were enrolled into this study. Patients were followed up for one-year period after being discharged from the hospital and were, then, divided into recurrent wheezing group and non-recurrent wheezing group based on whether there was recurrent wheezing or not. Wheezing or recurrent wheezing was specifically monitored in addition to blood tests for allergic and respiratory diseases. RESULTS: The prevalence of eczema and respiratory syncytial virus (RSV) infection were significantly higher in recurrent wheezing group than in control group (74.2% vs 45.8%; 32.3% vs. 13.3%, respectively, both P < 0.05); the percentage of blood eosinophil and serum eosinophil-derived neurotoxin (EDN) concentration at admission were also higher in recurrent wheezing group than in control group (3.10 ± 2.54% vs. 1.31 ± 1.15%; 68.67 ± 55.05 ng/mL vs. 27. 36 ± 19.51 ng/mL; respectively, both P < 0.001). Multivariate logistic regression analysis on eosinophil count and serum EDN concentration in predicting recurrent wheezing revealed that the eosinophil count showed the lowest sensitivity (51.6%) and highest specificity (90.4%), with the area under the ROC curve (AUC) of 0.752 ± 0.041; and that, in contrast, the serum EDN showed the highest sensitivity (88.7%) and lowest specificity (56.6%), with AUC of 0.795 ± 0.037. CONCLUSION: Combination of eosinophil count and serum EDN measurement may be better to predict the risk of recurrent wheezing in early life of childhood.

Refractory Mycoplasma pneumoniae pneumonia with concomitant acute cerebral infarction in a child: A case report and literature review.

RATIONALE: Mycoplasma pneumoniae pneumonia, a common cause of community-acquired pneumonia in children, is rarely complicated with acute cerebral infarction. PATIENT CONCERNS: We present a 7-year-old boy with severe M pneumoniae pneumonia who developed impaired consciousness, aphasia, and reduced limb muscle power 7 days postadmission. DIAGNOSES: Mycoplasma pneumoniae pneumonia with concomitant acute cerebral infarction. INTERVENTIONS: The patient recovered with aggressive antibiotic therapy, antiinflammation therapy with methylprednisolone, and gamma immunoglobulin and anticoagulation therapy with aspirin and low molecular weight heparin along with rehabilitation training. OUTCOMES: At 8 days postadmission, his consciousness was improved and at the 6-month follow-up visit, his muscle power of bilateral upper and lower limbs was normal except still poor right handgrip power. LESSONS: Stroke or cerebral infarction should be considered and promptly managed in rare cases of M pneumoniae pneumonia with neurologic manifestations.

Identification and characterization of human MIBP1 gene in glioma cell differentiation.

Malignant gliomas are the most common and lethal intracranial tumors; differentiation therapy is a promising candidate for their treatment. In order to reveal the mechanisms related to glioma differentiation, after confirming that differentiation was induced by sodium phenylbutyrate in SHG-44 human glioma cells, RNA arbitrary primer differential display was used to screen differentially expressed genes. One gene was found to be upregulated by differential display, and this was also confirmed by reverse northern blot and quantitative real-time PCR analysis. After it was cloned and sequenced, the 505-bp fragment was identified as the MIBP1 (c-myc intron-binding protein 1) gene, also named Hivep2/MBP-2/Schnurri-2. Quantitative real-time PCR analysis of 30 human tissue samples revealed that the expression of MIBP1 tended to decrease with increasing WHO grade and was significantly depressed in the high malignancy gliomas group (WHO grade IV). We cloned and sequenced the MIBP1 gene, which was accepted by GenBank as number DQ231041. Finally, transfection of MIBP1 in a reverse transcription vector into glioma cells inhibited cell growth, induced differentiation, and blocked the cell cycle. Here, we identify and describe the structure and function of a differentiation-related gene, human MIBP1, in human glioma.