To evaluate the clinical utility of targeted next-generation sequencing (tNGS) for pathogen detection of pediatric acute lower respiratory tract infections (ALRTIs), with a particular focus on the use of throat swab samples. In this diagnostic accuracy study involving 132 children, throat swabs and bronchoalveolar lavage fluid (BALF) samples were collected and analyzed by tNGS, and the results were compared with those obtained from conventional diagnostic methods. The impact of prior antibiotic use on the detection rate of tNGS was evaluated, the consistency between throat swabs and BALF was assessed, and the economic cost and invasiveness of the sampling methods were examined. This study enrolled 132 children, of whom 79 (60%) were boys and 53 (40%) were girls. Ninety-two (70%) of the patients had fever, and 128 (97%) had a cough. The detection rates of bacteria, viruses, and atypical pathogens in BALF samples by tNGS were 89.5% (n = 68), 98.2% (n = 108), and 77.8% (n = 63), respectively. Compared to traditional detection methods, tNGS showed significantly higher detection rates for bacteria and viruses (P < 0.001), but there was no statistically significant difference in the detection of atypical pathogens (P = 0.59). The use of antibiotics had no significant effect on bacterial detection by tNGS (P = 0.237). Using BALF-tNGS as the “gold standard,” the sensitivities of tNGS of throat swabs for detecting bacteria, viruses, and atypical pathogens were 95.83%, 88.16%, and 92.06%, respectively, with specificities of 55.95%, 83.93%, and 100%. In the analysis of economic costs and invasiveness, the cost of throat swab sampling was significantly lower than that of BALF sampling, and the associated pain score and complication rate were significantly lower (P < 0.05). tNGS with throat swabs offers higher sensitivity and specificity than traditional methods for diagnosing pediatric ALRTIs. As such, it offers a less invasive, more cost-effective alternative to BALF sampling.

In the diagnosis of bloodstream infections (BSI) in children, compared to the gold standard of blood culture, markers in the blood offer advantages such as rapid results and cost-effectiveness. Therefore, we investigated the clinical value of procalcitonin (PCT), C-reactive protein (CRP), white blood cell count (WBC), and neutrophil-to-lymphocyte ratio (NLR) in the early diagnosis of BSI in children. This study included a retrospective analysis of 309 suspected BSI cases and patients were categorized into 2 groups based on blood culture results: blood culture-positive group, and blood culture-negative group. The blood culture-positive group was further partitioned into 3 sub-groups based on the type of pathogen: Gram-positive (G +) bacteria, Gram-negative (G-) bacteria, and fungi. Changes in PCT, CRP, WBC, and NLR were evaluated, and pathogen infections among these aforementioned groups were further determined. Moreover, the study employed the receiver operating characteristic (ROC) curve to evaluate the diagnostic value of these indicators in identifying BSI in pediatric patients at an early stage. Among the 98 strains of pathogens detected in blood culture, 58 (58.2%) strains were G- bacteria, 33 (33.7%) strains were G + bacteria, and 7 (7.1%) strains were fungi. The levels of PCT, CRP, WBC, and NLR were found to be significantly higher in the blood culture-positive group than the blood culture-negative group (p < 0.01). Upon comparing the levels of PCT and CRP in the three pathogen infections, it was found that the fungi group exhibited higher levels than the G- and G + bacteria groups (p < 0.01). The G- bacteria group exhibited higher levels of PCT, CRP, and WBC than the blood culture-negative group (p < 0.05). Similarly, the G + bacteria group exhibited higher levels of PCT, WBC, and NLR than the blood culture-negative group (p < 0.01). Besides, PCT presented the highest diagnostic efficiency among the single-item detections, with an AUC of 0.862 (95% CI: 0.819–0.906). The simultaneous detection of multiple parameters does not necessarily improve diagnostic performance but can enhance detection sensitivity. PCT and CRP can provide important complementary information for the etiological diagnosis of BSI in children. Elevated levels of PCT and CRP were often associated with fungal or G- bacterial infections, with PCT showing particularly significant effects. Combined use of serum PCT, CRP, WBC, and NLR testing can improve the diagnostic sensitivity of pediatric BSI, reducing the risk of missed diagnoses, thereby enhancing the early diagnostic value of pediatric BSI.

To explore the effect of targeted second-generation sequencing technique to guide clinical diagnosis and medication on the therapeutic effect and prognosis of respiratory tract infection (RTI) in children. During January 2021 to June 2022, 320 children with RTI cured were selected in our hospital as the object of this retrospective study. The control group accepted empirical broad-spectrum antibacterial therapy and the observation group accepted targeted second-generation sequencing technique to guide diagnosis and medication. The therapeutic effect, improvement time of clinical symptom index, laboratory-related index, level of inflammatory factors, incidence of complications, and parents’ treatment satisfaction were compared. The observation group was considerably more efficacious (91.25%) versus the controlled group (72.50%). The duration of enhancement of fever, nasal congestion, tonsillar congestion, and cough symptoms was shorter in the observation group (P < .05). Serum levels of iron, IgA, IgG as well as IgM were substantially elevated in the observation group. The levels of IL-4 and IL-10 were markedly reduced in the observation group after treatment. The prevalence of complications was considerably below that of the comparison group (21.25%) in the observation group (8.75%). Parental satisfaction with therapy was markedly higher in the observation group (92.50%) than in the control group (66.25%). The application of targeted second-generation sequencing technology to guide clinical diagnosis and drug use can elevate the RTIs efficacy and prognosis in childhood. Targeted second-generation sequencing can achieve precise treatment, reduce drug resistance of drug-resistant strains, and improve the efficacy. It has high promotion and application value.

Urban environmental and climatic conditions are closely linked to pediatric respiratory health, yet their associations with specific infectious diseases such as mycoplasma pneumoniae pneumonia (MPP) remain poorly understood. To address this gap, this study investigated the spatiotemporal dynamics of pediatric MPP across 46 urban subdistricts in Fuzhou, China. Using global and local spatial autocorrelation, space–time scan statistics, and interpretable machine learning models, we assessed both incidence patterns and progression to severe disease, as well as their built-environment correlates. Key findings include: (1) A pronounced seasonality of MPP is observed, with autumn–winter peaks and summer troughs. High-risk clusters are concentrated in central urban areas, whereas peripheral regions exhibit weaker clustering. These patterns are consistent with variations in climate, population density, transport networks, greenspace, and school-term cycles. (2) The autumn–winter period represents the critical risk window, encompassing a large share of cases and repeatedly centering on repeatedly high-incidence subdistricts. Demographically, boys and children aged 5–9 years face the highest risk, whereas the 10–14 age group—particularly girls—shows the lowest incidence. (3) Machine learning identified age, near-road exposure, and selected land-use/amenity intensities as core predictors, especially for severe MPP cases. Age exhibits a non-monotonic incidence profile, while distance from primary roads follows a steep inverse gradient, with elevated risk confined to a 100–150 m roadside buffer zone. School density shows a monotonic positive association, whereas greenspace is a broadly negative association. These findings may help inform seasonally tailored, microscale interventions, strengthened school health monitoring, and targeted urban design in high-prevalence corridors.

Objective: To investigate the self-reported understanding and attitudes of healthcare workers and caregivers toward the application of artificial intelligence (AI) in pediatric healthcare in a children’s specialty hospital, and to compare differences between the two groups. Methods: This single-center cross-sectional questionnaire survey was conducted at Fujian Children’s Hospital from January 2026 to June 2026. A self-designed questionnaire was used to assess healthcare workers’ and caregivers’ self-reported understanding, trust, risk perception, and willingness to accept AI applications in pediatric healthcare. Respondents were divided into groups according to their identity. Categorical variables were compared using the chi-square test or Fisher’s exact test, while continuous variables were compared using the independent-samples t test or the Mann–Whitney U test. Results: A total of 261 respondents were included, comprising 159 caregivers and 102 healthcare workers. Healthcare workers had a higher level of self-reported understanding and a higher frequency of use of AI applications in pediatric healthcare than caregivers ( p < 0.01); however, they also showed stronger perceptions of data security risks ( p < 0.001). Caregivers reported greater trust in AI-assisted medical decision-making than healthcare workers ( p < 0.001). Most respondents believed that AI could not replace pediatric healthcare workers, with no statistically significant difference between the two groups ( p = 0.166). Regarding potential advantages, healthcare workers were more likely to agree that AI could reduce human error ( p < 0.001). Regarding potential limitations, healthcare workers expressed greater concern about the risks of misdiagnosis, lack of humanistic care, and data privacy and security issues ( p < 0.001). Conclusion: In this single-center children’s specialty hospital, healthcare workers reported greater self-rated understanding and more frequent use of AI, together with stronger awareness of several potential risks, whereas caregivers showed greater trust in AI-assisted medical decision-making despite lower self-rated understanding. These findings should be interpreted cautiously and require confirmation in larger multicenter studies before being generalized to broader populations in China.

Objectives: Respiratory Syncytial Virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with tNGS-confirmed RSV infection. Patients were randomly divided into a training set (n=363) and a test set (n=155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-Lymphocyte Ratio (NLR), C-Reactive Protein (CRP), and Serum Amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (P > 0.05). Decision Curve Analysis demonstrated net clinical benefit across 10%–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes.