RESUMO
Acute respiratory tract infections (ARTI) are caused by respiratory pathogens and range from asymptomatic infections to severe respiratory diseases. These diseases can be life threatening with high morbidity and mortality worldwide. Under the pandemic of coronavirus disease 2019 (COVID-19), little has been reported about the pathogen etiologies and epidemiology of patients suffering from ARTI of all age in Xiamen. Region-specific surveillance in individuals with ARTI of all ages was performed in Xiamen from January 2020 to October 2022. Here, we observed the epidemiological characteristics of thirteen pathogens within ARTI patients and further revealed the difference of that between upper respiratory tract infections (URTI) and lower respiratory tract infections (LRTI). In total 56.36 % (2358/4184) of the ARTI patients were positive for at least one respiratory pathogen. Rhinovirus (RVs, 29.22 %), influenza A (FluA, 19.59 %), respiratory syncytial virus (RSV, 18.36 %), metapneumovirus (MPV, 13.91 %), and adenovirus (ADV, 10.31 %) were the five leading respiratory pathogens. Respiratory pathogens displayed age- and season-specific patterns, even between URTI and LRTI. Compared with other groups, a higher proportion of FluA (52.17 % and 68.75 %, respectively) infection was found in the adult group and the elder group, while the lower proportion of RVs (14.11 % and 11.11 %) infection was also observed in them. Although ARTI cases circulated throughout the year, RVs, FluB, and BoV peaked in autumn, and FluA circulated more in summer. Besides, the co-infectious rate was 8.7 % with the most common for RVs. Logistic regression analyses revealed the correlations between respiratory pathogens and disease types. These results are essential for replenishing epidemiological characteristics of common respiratory pathogens that caused ARTI in Xiamen during the epidemic of COVID-19, and a better understanding of it might optimize the local prevention and clinical control.
RESUMO
BACKGROUND We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL AND METHODS RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings. RESULTS On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration. CONCLUSIONS Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.