Early identification of septic shock in children with sepsis remains challenging, particularly in resource-limited and geographically disadvantaged settings. The predictive performance of C-reactive protein, procalcitonin, and blood lactate for septic shock was evaluated in a multicenter retrospective study involving 245 children aged <10 years who were diagnosed with sepsis at six provincial hospitals in northern mountainous Vietnam between 2022 and 2025. Patients were classified into septic shock (n = 82) and non-septic shock (n = 163) groups. Clinical characteristics and C-reactive protein, procalcitonin, and blood lactate concentrations measured within 24 hours of hospital admission were evaluated. Biomarker discrimination was assessed using receiver operating characteristic curve analysis. The median age of the study population was 35 months (interquartile range, 18.0–54.0 months), and septic shock was identified in 82 children (33.5%). Fever >38.5 °C (adjusted odds ratio, 3.91; 95% confidence interval, 1.62–9.43), cardiovascular dysfunction (adjusted odds ratio, 10.94; 95% confidence interval, 5.19–23.08), and neurological dysfunction (adjusted odds ratio, 6.07; 95% confidence interval, 2.80–13.16) were independently associated with septic shock. Among the three biomarkers evaluated, blood lactate showed the greatest discriminatory ability, with an area under the receiver operating characteristic curve (AUC) of 0.713 (95% confidence interval, 0.640–0.785) and an optimal cutoff of 3.93 mmol/L. Procalcitonin showed lower discriminatory performance (AUC, 0.652; 95% confidence interval, 0.577–0.727), whereas C-reactive protein did not discriminate between patients with and without septic shock (AUC, 0.485; p = 0.711). These findings suggest that, among the biomarkers evaluated, blood lactate provides the greatest discriminatory value for septic shock in children with sepsis. Procalcitonin may provide complementary information, whereas C-reactive protein appears to have limited discriminatory utility for this purpose. Blood lactate, potentially supplemented by procalcitonin and readily assessed clinical indicators, may therefore contribute to early risk stratification for septic shock in pediatric sepsis, particularly in resource-limited healthcare settings.

Accurate classification of brain tumors from magnetic resonance imaging (MRI) is important for supporting timely diagnosis and subsequent clinical decision-making. In this study, EnDeC, an enhanced convolutional neural network, was developed for automated multiclass classification of brain MRI images. The Crystal Clean: Brain Tumors MRI Dataset, comprising 21,672 images categorized as glioma, meningioma, pituitary tumor, or normal, was used for model development and evaluation. Following data cleaning and class balancing, 12,264 images were retained for model training. Image preprocessing and augmentation procedures, including resizing, noise injection, and rotation, were applied. An efficient input pipeline was implemented using the TensorFlow tf.data application programming interface, with caching and prefetching incorporated to reduce input/output bottlenecks during training. The EnDeC architecture was constructed using successive convolutional and pooling layers for hierarchical feature extraction, followed by fully connected layers for multiclass classification. Model performance was evaluated using accuracy, precision, recall, F1-score, and specificity. Average values of 99.60%, 99.18%, 99.19%, 99.18%, and 99.74% were obtained for these metrics, respectively. Among the evaluated optimization configurations, the Adam optimizer yielded the highest test accuracy of 99.20%. These findings demonstrate that discriminative imaging features associated with multiple brain tumor categories can be effectively learned using the proposed convolutional neural network framework. Nevertheless, validation using independent, patient-level, multi-institutional datasets is required before clinical applicability can be established. The proposed approach provides a computational framework for automated brain MRI classification and may support the further development of computer-aided diagnostic systems for brain tumor assessment.