[Analysis of non-bacterial respiratory pathogens in children in Ningbo City from 2019 to 2021].

Objective: To investigate the distribution characteristics of respiratory non-bacterial pathogens in children in Ningbo from 2019 to 2021. Methods: A retrospective analysis was performed on 23 733 children with respiratory tract infection who visited the department of pediatrics of Ningbo Women and Children's Hospital from July 2019 to December 2021. There were 13 509 males (56.92%) and 10 224 females (43.08%), with an age range of 1 day to 18 years old. There were 981 cases in the neonatal group (younger than 1 month old), 5 880 cases in the infant group (1 month to younger than 1 year old), 6 552 cases in the toddler group (1 to younger than 3 years old), 7 638 cases in the preschool group (3 to younger than 7 years old), and 2 682 cases in the school-age group (7 to 18 years old). Thirteen respiratory pathogens were detected by multiple polymerase chain reaction (PCR) based on capillary electrophoresis, and SPSS 23.0 software was used for statistical analysis of the results, the count data were expressed as percentages, and the χ2 test was used for comparison between groups. Results: Of the 23 733 specimens, 13 330 were positive for respiratory pathogens, with a total positive rate of 56.17%. The positive rates of human rhinovirus (HRV) 24.05% (5 707/23 733), human respiratory syncytial virus (HRSV) 10.45% (2 480/2 3733) and mycoplasma pneumoniae (Mp) 7.03% (1 668/23 733) were in the first three. The positive rates of pathogens in the male and female children were 57.47% (7 763/13 509) and 54.45% (5 567/10 224), respectively, and the difference was statistically significant (χ2=21.488, P<0.001). The positive rates in the neonatal group, infant group, toddler group, preschool group, and school-age group were 31.80% (312/981), 54.71% (3 217/5 880), 63.23% (4 143/6 552), 59.83% (4 570/7 638), 40.57% (1 088/2 682), respectively, and the difference among the groups was statistically significant (χ2=681.225, P<0.001). The single infection rate was 47.43% (11 256/23 733), the mixed infection rate of two or more pathogens was 8.74% (2 074/23 733), most of which were mixed infections of two pathogens. HRV, HADV, HCOV, Ch disseminated in the whole year. HRSV, HMPV, Boca, HPIV occurred mostly in fall and winter. The positive rates of FluA, FluB, Mp were at a low level after the corona virus disease 2019 (COVID-19) epidemic (2020 and 2021). The positive rates of FluA, H1N1, H3N2, FluB, HADV, Mp in 2020 were significantly lower than in 2019 (P<0.05). The positive rates of HPIV, HRV, HCOV, Ch in 2020 were significantly higher than in 2019 (P<0.05). The positive rates of FluA, H1N1, H3N2, HPIV, HCOV, Mp, Ch in 2021 were significantly lower than in 2020 (P<0.05). The positive rates of Boca, HMPV, HRSV in 2021 were significantly higher than in 2020 (P<0.05). Conclusion: From 2019 to 2021, the main non-bacterial respiratory pathogens of children in Ningbo City were Mp and HRV, and the detection rates of respiratory pathogens varied among different ages, seasons and genders.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

[Preliminary study of the relationship between novel coronavirus pneumonia and liver function damage: a multicenter study].

Objective: To analyze the clinical characteristics of cases of novel coronavirus pneumonia and a preliminary study to explore the relationship between different clinical classification and liver damage. Methods: Consecutively confirmed novel coronavirus infection cases admitted to seven designated hospitals during January 23, 2020 to February 8, 2020 were included. Clinical classification (mild, moderate, severe, and critical) was carried out according to the diagnosis and treatment program of novel coronavirus pneumonia (Trial Fifth Edition) issued by the National Health Commission. The research data were analyzed using SPSS19.0 statistical software. Quantitative data were expressed as median (interquartile range), and qualitative data were expressed as frequency and rate. Results: 32 confirmed cases that met the inclusion criteria were included. 28 cases were of mild or moderate type (87.50%), and four cases (12.50%) of severe or critical type. Four cases (12.5%) were combined with one underlying disease (bronchial asthma, coronary heart disease, malignant tumor, chronic kidney disease), and one case (3.13%) was simultaneously combined with high blood pressure and malignant tumor. The results of laboratory examination showed that the alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), and total bilirubin (TBil) for entire cohort were 26.98 (16.88 ~ 46.09) U/L and 24.75 (18.71 ~ 31.79) U/L, 39.00 (36.20 ~ 44.20) g/L and 16.40 (11.34 ~ 21.15) µmol/L, respectively. ALT, AST, ALB and TBil of the mild or moderate subgroups were 22.75 (16.31 ~ 37.25) U/L, 23.63 (18.71 ~ 26.50) U/L, 39.70 (36.50 ~ 46.10) g/L, and 15.95 (11.34 ~ 20.83) µmol/L, respectively. ALT, AST, ALB and TBil of the severe or critical subgroups were 60.25 (40.88 ~ 68.90) U/L, 37.00 (20.88 ~ 64.45) U/L, 35.75 (28.68 ~ 42.00) g/L, and 20.50 (11.28 ~ 25.00) µmol/L, respectively. Conclusion: The results of this multicenter retrospective study suggests that novel coronavirus pneumonia combined with liver damage is more likely to be caused by adverse drug reactions and systemic inflammation in severe patients receiving medical treatment. Therefore, liver function monitoring and evaluation should be strengthened during the treatment of such patients.