BACKGROUND: Children's respiratory health demonstrates particular sensitivity to air pollution. Existing evidence investigating the association between short-term ozone (O₃) exposure and childhood pneumonia remains insufficient and inconsistent, especially in low- and middle-income countries (LMICs). METHOD: To provide more reliable and persuasive evidence, we implemented a multi-city, time-stratified case-crossover design with a large sample size, using data from seven representative children's hospitals across major geographical regions in China. To avoid the impact of the COVID-19 pandemic, individual-level medical records of inpatient children under 6 years of age diagnosed with pneumonia during 2016-2019 were collected. Conditional logistic regression models were fitted for each city, and city-specific estimates were pooled through a meta-analysis using a random-effects model. RESULTS: In total, the study included 137,470 pediatric pneumonia hospital admissions. The highest pooled estimate for O₃ occurred at lag0-1, with a 10 µg/m³ increase in O₃ associated with a 1.57% (95% CI: 0.67%-2.48%) higher risk of pediatric pneumonia hospital admissions. Stratified analyses indicated that the effects of O₃ were robust across different sexes, age groups, and admission seasons. We also observed a statistically significant increase in risk associated with O₃ concentrations exceeding the World Health Organization Air Quality Guidelines (WHO-AQGs). CONCLUSIONS This study revealed a significant positive association between O₃ and pediatric pneumonia hospital admissions. Our findings substantially strengthen the evidence base for the adverse health impacts of O₃, underscoring the importance of O₃ pollution control and management in reducing the public health burden of pediatric pneumonia.
Humans ; Ozone/analysis* ; China/epidemiology* ; Pneumonia/chemically induced* ; Child, Preschool ; Male ; Female ; Infant ; Cross-Over Studies ; Air Pollutants/analysis* ; Hospitalization/statistics & numerical data* ; Child ; Cities/epidemiology* ; Air Pollution/adverse effects* ; Infant, Newborn ; Environmental Exposure/adverse effects*

OBJECTIVE: To investigate the inhibitory effect of Tanreqing Injection (TRQ) on the activation of nucleotide-binding oligomerization domain-like receptor pyrin domain containing 3 (NLRP3) inflammasome in macrophages infected with influenza A virus and the underlying mechanism based on mitophagy pathway. METHODS: The inflammatory model of murine macrophage J774A.1 induced by influenza A virus [strain A/Puerto Rico/8/1934 (H1N1), PR8] was constructed and treated by TRQ, while the mitochondria-targeted antioxidant Mito-TEMPO and autophagy specific inhibitor 3-methyladenine (3-MA) were used as controls to intensively study the anti-inflammatory mechanism of TRQ based on mitophagy-mitochondrial reactive oxygen species (mtROS)-NLRP3 inflammasome pathway. The levels of NLRP3, Caspase-1 p20, microtubule-associated protein 1 light chain 3 II (LC3II) and P62 proteins were measured by Western blot. The release of interleukin-1β (IL-1β) was tested by enzyme linked immunosorbent assay, the mtROS level was detected by flow cytometry, and the immunofluorescence and co-localization of LC3 and mitochondria were observed under confocal laser scanning microscopy. RESULTS: Similar to the effect of Mito-TEMPO and contrary to the results of 3-MA treatment, TRQ could significantly reduce the expressions of NLRP3, Caspase-1 p20, and autophagy adaptor P62, promote the expression of autophagy marker LC3II, enhance the mitochondrial fluorescence intensity, and inhibit the release of mtROS and IL-1β (all P<0.01). Moreover, LC3 was co-localized with mitochondria, confirming the type of mitophagy. CONCLUSION TRQ could reduce the level of mtROS by promoting mitophagy in macrophages infected with influenza A virus, thus inhibiting the activation of NLRP3 inflammasome and the release of IL-1β, and attenuating the inflammatory response.
Mitophagy/drug effects* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Animals ; Macrophages/virology* ; Inflammasomes/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Mitochondria/metabolism* ; Reactive Oxygen Species/metabolism* ; Influenza A virus/physiology* ; Interleukin-1beta/metabolism* ; Cell Line ; Injections

Critically ill patients often experience significant skeletal muscle wasting due to prolonged bed rest, metabolic disorders, inflammatory responses and malnutrition, which affects the patient's mobility and may also lead to increased mortality. Timely and accurate assessment of muscle status is important for optimizing treatment strategies and improving patient prognosis. There are various limitations in the current methods of assessing muscle mass, and muscle ultrasound, as a noninvasive, convenient, low-cost and suitable technique for bedside monitoring, has received increasing attention for its application in muscle assessment of critically ill patients. However, there are still a number of challenges in its practical application, such as the lack of uniform standards for the measurement method, the high dependence on the operation, and the reproducibility of the data that needs to be optimized, and so on. The aim of this article is to systematize the research progress of muscle ultrasound in muscle assessment of critically ill patients, and to discuss the advantages and limitations of its clinical application, in order to provide a scientific basis for future research and clinical practice.
Humans ; Critical Illness ; Ultrasonography ; Muscle, Skeletal/diagnostic imaging*

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Kv1.3,a voltage-gated potassium channel,is highly expressed in T lymphocytes,the nervous system,and vascular smooth muscle cells.It plays a critical role in membrane excitability and electrical signal transduction,serving as an important target for studying T-cell function and providing a promising direction for developing therapeutics against autoimmune and inflammatory diseases.Therefore,the de-velopment of specific inhibitors of Kv1.3 channel has emerged as a novel therapeutic strategy for these disorders.In this study,we isolated and purified a novel Kv1.3-inhibitory peptide toxin,LmKTx13,from the venom of the scorpion Lychas mucronatus using reversed-phase high-performance liquid chroma-tography(RP-HPLC).LmKTx13 consists of 38 amino acid residues,including six cysteines that form three disulfide bonds.Whole-cell patch-clamp recordings revealed that LmKTx13 potently inhibited Kv1.3 with an IC50 of 7.92±3.0 nmol/L.Selectivity analysis showed that 2 μmol/L LmKTx13 also in-hibited Kv1.2 and Kv1.7,but exhibited no significant effects on other potassium channel subtypes or voltage-gated sodium channels.Further investigation into the mechanism demonstrated that LmKTx13 acts as a pore-blocking inhibitor of Kv1.3.By analyzing the effects of LmKTx13 on Kv1.3 channel gating ki-netics and performing sequence alignment of the pore regions of Kv1.3 and Kv1.5,we constructed site-directed mutants and identified the pore region of Kv1.3 as the critical binding site for LmKTx13.Key residues involved in the interaction included T425,G427,and H451.In summary,we discovered a no-vel pore-blocking Kv1.3 inhibitor,LmKTx13,from L.mucronatus venom,which exhibits high affinity and selectivity for Kv1.3.These findings highlight its potential as a potential lead molecule for developing Kv1.3-targeted therapeutics.