Objective: With the global epidemic of coronavirus disease 2019 (COVID-19), vaccination rates are increasing globally. This study evaluated the relevant clinical manifestations of vaccinated COVID-19 patients. Methods: We searched carefully in 11 databases such as PubMed, Embase, Scopus, Cochrane Library, Web of Science, Ovid, China National Knowledge Infrastructure Database, Wan Fang Data, Sinomed, VIP Database, and Reading Showing Database up to 26 March 2022. To search for articles that have described the characteristics of vaccinated patients including epidemiological and clinical symptoms. Statistical analysis of the extracted data using STATA 14.0. Results: A total of 58 articles and 263,708 laboratory-confirmed COVID-19 patients were included. Most of the patients in the vaccinated group had more asymptomatic infection and fewer severe illnesses. There were significant differences in ethnicity, and strain infected with COVID-19, and comorbidities (hyperlipidemia, diabetes, obesity, kidney disease, immunocompromised, cardiovascular disease, and tumor) and symptoms (fever, cough, gastrointestinal symptoms, neurological symptoms, and dysgeusia/anosmia) between vaccinated group and unvaccinated group. Oxygen support, use of steroid, days in hospital, hospital treatment, ICU treatment, death, and poor prognosis were also significantly different. Conclusion: Compared with the vaccinated group, patients in the unvaccinated group had a more severe clinical manifestations. Vaccines are also protective for infected people.

Some patients with viral encephalitis in China seek treatment with Chinese patent medicine (CPM) to improve their symptoms, but few studies have focused on the impact of CPM on the prognosis of viral encephalitis (VE). The aim of this multicenter retrospective study was to assess the benefit of adjunctive CPM therapy on the outcome of children with VE in China. This study retrospectively included 834 children with viral encephalitis who were hospitalized at five medical institutions from 2018 to 2021. Univariate and multivariate logistic regression was used to assess the effect of CPM on sequelae in patients with VE. 1:1 propensity score matching was used to exclude the effect of confounding factors. Forest plots were used to observe the effect of CPM on the prognosis of VE in different subgroups. There were fewer patients with sequelae in the group of patients using CPM regardless of whether they were matched or not. The results of multivariate logistic regression analysis showed that the use of CPM was an independent protective factor for the development of sequelae in VE patients (OR = 0.063, 95 % CI: 0.011-0.350, p = 0.002). Subgroup analyses showed that CPM was a protective factor for the development of sequelae regardless of the presence or absence of coma and comorbidities. In addition, we evaluated other outcome indicators and found shorter duration of illness, fever and headache in children with EV in the CPM group. Adjunctive CPM therapy may significantly reduce sequelae in children with VE, as well as effectively alleviate patients' clinical symptoms. However, more prospective studies and clinical trials are needed to further evaluate its efficacy and safety.

Background: Viral encephalitis (VE), a central nervous system disorder with high mortality and disability rates, poses a serious threat to childhood development. Xiyanping injection (XYPI), an andrographolide sulfonate preparation widely used in China, exhibits anti-inflammatory, antiviral, antitumor, antibacterial and neuroprotective properties. Methods: We conducted a retrospective study of 635 pediatric inpatients with VE who were hospitalized at seven medical centers in China between January 2015 and December 2021. Risk factors for poor prognosis were compared between inpatients treated with XYPI (n = 480) and those without XYPI treatment (n = 155). Propensity score matching was performed to reduce potential confounding. Clinical symptoms, hospitalization costs, complications and sequelae were evaluated simultaneously. Results: Multivariate Logistic regression identified XYPI treatment as an independent protective factor for poor prognosis (odds ratio [OR] = 0.251, 95% confidence interval [CI]: 0.113–0.559, p < 0.001). XYPI significantly shortened the duration of fever and headache, reduced hospitalization costs, and lower the incidences of respiratory infections, myocardial injury, and sequelae (all p < 0.05). Conclusion: Adjuvant XYPI therapy may improve clinical outcomes and reduce the economic burden in pediatric VE; however, randomized trials are warranted to validate these findings.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with significant mortality risks. This study evaluated the predictive value of the lactate dehydrogenase/albumin to urea ratio (LAU) for lethal outcomes in SFTS patients. Clinical data from 348 patients (295 survivors, 53 non‐survivors) admitted to Yantai Infectious Disease Hospital (2022‐2023) were analyzed. Multivariate analysis identified older age, SFTS‐associated encephalopathy, SFTSV RNA, decreased albumin and serum calcium, increased lactate dehydrogenase, and elevated LAU as independent mortality risk factors. LAU demonstrated strong predictive accuracy (AUC: 0.79; 95% CI: 0.72‐0.85; sensitivity 69%, specificity 75%), with an optimal cutoff of 144.685 U·mmol/g·L. Survival analysis linked high LAU to poor prognosis, and longitudinal trends showed decreasing LAU with recovery in survivors but rising levels in non‐survivors. These findings highlight LAU as a novel, practical biomarker for early risk stratification and prognosis improvement in SFTS.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high lethality. This study aimed to determine whether prolonged activated partial thromboplastin time (APTT) predicted SFTS mortality. SFTS patients were enrolled from 6 hospitals in the north China. Subjects were divided into training cohort and 5 externally validation cohorts. The least absolute shrinkage and selection operator Cox regression model was performed to screen potential prognostic factors. Risk factors were analyzed using multivariable regression models. Prognostic models were established by Cox regression and random survival forest (RSF) methods, and evaluated regarding discrimination, validity and clinical benefit. Time-dependent receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of variables. 1332 SFTS cases were included, in which 211 patients died. Six potential prognostic factors were screened, and pulse, breath, APTT and aspartic transaminase (AST) were independently associated with mortality in both training cohort (Yantai, N = 791) and external validation cohort (N = 541). APTT was steadily correlated with the fatality (HR: 1.039-1.144; all P < 0.01) in each five sub-validation cohorts (Dandong, Dalian, Tai'an, Qingdao and Beijing). RSF model with variables of APTT, AST, pulse and breath had considerable prognostic effectiveness, which APTT showed the highest prognostic ability with the area under the curve of 0.848 and 0.787 for 7-day and 14-day survival, respectively. Survival differences were found between high and low levels of APTT for mortality using 50s as the optimal cut-off. SFTS patients have prolonged APTT, which is an independent risk factor for fatality. APTT≥50s was recommended as a biomarker to remind physicians to monitor and treat patients more aggressively to improve clinical prognosis.

Viral respiratory infections (VRTIs) caused by influenza (Flu) and COVID-19 pose significant global health challenges. Clinical outcomes are further exacerbated by infections with hospital acquired drug resistant pathogens (DRPs). A retrospective analysis was conducted on the data of 1,051 hospitalized patients with VRTIs from 2018 to 2024 at Beijing Ditan Hospital. Firstly, 280 drug-resistant strains were isolated from 185 patients with hospital-acquired DRPs infections for extended antibiogram analysis. Secondly, Interpretable machine learning (ML) was employed to predict the risk factors for hospital acquired DRPs infections in patients with VRTIs. Using the optimal feature subset, seven ML prediction models were developed. Parameter tuning was performed via 10-fold cross-validation and grid search. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, precision, and F1 score. SHAP (SHapley Additive exPlanations) was used to interpret the optimal model. Pathogen distribution in 280 clinical samples revealed sputum (65.36%) as themainsource, followed byblood (15.36%), urine (11.43%), and lavage fluid (5.00%). In all clinical specimens, Pseudomonas aeruginosa, Staphylococcus hominis, Escherichia coli, and Acinetobacter baumannii predominated in sputum, blood, urine, and lavage fluid, respectively. In terms of overall detection counts, the most frequently isolated strains were P. aeruginosa, Klebsiella pneumoniae, and A. baumannii. The drug resistance rate of P. aeruginosa to third-generation cephalosporins (such as ceftriaxone and cefotaxime) exceeds 89%, but it has relatively higher sensitivity to ceftazidime (71.7%) and cefepime (69.6%). Its drug resistance rates to imipenem and meropenem reach 45.7%. Although amikacin shows 100% sensitivity, combination with β-lactam antibiotics is recommended to reduce mortality. K. pneumoniae shows resistance rates of 53.3% to imipenem and 46.7% to meropenem, with over 50% resistance to levofloxacin and ciprofloxacin. Effective agents include sulfamethoxazole (68.9% susceptible), tigecycline (64.4%), chloramphenicol (62.2%), and amikacin (62.2%). Tigecycline combined with aminoglycosides has synergistic effects and inhibits resistant strains. A. baumannii was highly resistant to nearly all tested antibiotics, showing only partial susceptibility to minocycline (59.5%) and trimethoprim-sulfamethoxazole (38.1%). Among the seven ML models, the neural network (NN) achieved the best predictive performance. The SHAP method revealed the top 15 predictive variables by importance ranking, including length of stay (LOS), cholinesterase (CHE), age, albumin (ALB), etc.