Purulent pericarditis is a rare occurrence in the era of modern antibiotics. It is most often caused by organisms such as Staphylococcus aureus, Streptococcus pneumoniae, Viridans streptococci, Haemophilus influenzae and anaerobic bacteria with Streptococcus pyogenes (S.pyogenes) being a possible, though very uncommon etiology. This case represents an occurrence of S. pyogenes pericarditis in an apparently healthy female with no known immunocompromising condition. A 37-year-old female, married, real estate agent with no comorbidities came in for chest pain radiating to the upper back, relieved with leaning forward. Cardiac biomarker was normal, ECG demonstrated diffuse ST-segment elevation and PR segment depression, while imaging showed lobar pneumonia. Blood tests showed leukocytosis with neutrophilic predominance and workup for immunocompromised state was negative. The 2D echo showed large pericardial effusion with tamponade physiology. An urgent pericardiocentesis was done. Cultures grew Streptococcus pyogenes confirming the diagnosis of acute purulent pericarditis. Daily drainage of pericardial effusion, colchicine, ibuprofen was initiated together with antibiotics and the patient had resolution of pericardial effusion. Acute pyogenic pericarditis with cardiac tamponade is a rare but serious condition that requires prompt recognition and intervention. Early diagnosis, through a combination of clinical suspicion, ECG and echocardiography is crucial for initiating timely treatment.

Human ; Female ; Adult: 25-44 Yrs Old ; Viridans Streptococci ; Streptococcus Pyogenes ; Streptococcus Pneumoniae ; Staphylococcus Aureus ; Streptococcal Infections ; Electrocardiography ; Echocardiography ; Early Diagnosis

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

BACKGROUND: Most studies have evaluated disability-adjusted life years (DALYs) of colorectal cancer (CRC) patients based on a set of generic disability weights (DWs). This study aimed to apply local CRC-stage-specific DWs to estimate the burden of DALYs for CRC (CRC-DALYs) in populations in China and consider the influence of local screening coverage of CRC. METHODS: A prevalence-based model was constructed using data from various sources. Years lived with disability (YLDs) were estimated mainly via cumulative prevalence data (based on CRC incidence rates, population numbers, and survival rates), stage-specific proportions of CRC, and DWs of the local population. Years of life lost (YLLs) were calculated based on the CRC mortality rates and standard life expectancies. CRC incidence and mortality rates for the years 2020, 2025, and 2030 were estimated by joinpoint regression, and the corresponding DALYs were predicted. The main assumption was made for CRC screening coverage. Sensitivity analyses were used to assess the impact of population, DWs, and coverage. RESULTS: In 2017, among the Chinese population, the estimated number of CRC-DALYs was 4,303,314 (11.9% for YLDs). If CRC screening coverage rate in China (2.3%) remains unchanged, the overall DALYs in 2030 are predicted to increase by 37.2% (45.1% of those aged ≥65 years). More optimistically, the DALYs would then decrease by 0.7% in 2030 (from 5,902,454 to 5,860,200) if the coverage could be increased to 25.0%. A sensitivity analysis revealed that using local DWs would change the base-case values by 5.7%. CONCLUSIONS The estimated CRC-DALYs in China using population-specific DWs were considerably lower (with a higher percentage of YLDs) than the global burden of disease (GBD) estimates (5,865,004, of 4.6% for YLDs), suggesting the impact extent of applying local parameters. Sustainable scale-up CRC screening needs to be in place to moderate the growth trend of CRC-DALYs in China.
Humans ; Colorectal Neoplasms/diagnosis* ; China/epidemiology* ; Disability-Adjusted Life Years ; Male ; Prevalence ; Female ; Middle Aged ; Aged ; Early Detection of Cancer ; Quality-Adjusted Life Years ; Adult ; Incidence

Humans ; Language Development Disorders/diagnosis* ; Early Diagnosis ; Child

Humans ; Bronchiolitis Obliterans/etiology* ; Early Diagnosis ; Pneumonia, Mycoplasma/complications* ; Consensus ; Child ; Mycoplasma pneumoniae ; China/epidemiology* ; Mass Screening ; Adenoviridae Infections/complications*

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Achondroplasia (ACH) is a common skeletal dysplasia in children, primarily caused by mutations in the fibroblast growth factor receptor 3 (FGFR3) gene. These mutations disrupt the process of endochondral ossification in different types of bones, including long bones of the limbs and vertebrae. Children with ACH typically present with short stature and may experience severe multi-system complications. The diagnosis of ACH is based on typical clinical manifestations, imaging features, and genetic testing results. Treatment options mainly include pharmacological interventions and surgical procedures aimed at improving height, as well as symptomatic management for associated complications. This article discusses both prenatal and clinical diagnostic approaches for ACH, as well as treatment strategies focused on enhancing height, aiming to deepen the understanding of this condition.
Humans ; Achondroplasia/therapy* ; Child ; Body Height ; Early Diagnosis ; Receptor, Fibroblast Growth Factor, Type 3/genetics*

OBJECTIVES: To investigate the clinical and immunological features of children with primary immune thrombocytopenia (pITP) or connective tissue disease (CTD) with thrombocytopenia as the initial manifestation at initial diagnosis, and to provide a basis for early differentiation. METHODS: A retrospective study was performed on 236 children with pITP (pITP group) or CTD with thrombocytopenia as the initial manifestation (CTD-TP group) who were admitted from January 2019 to August 2024. Clinical and immunological indicators were compared between the two groups to identify potential influencing factors for early differentiation and their discriminative validity. RESULTS: Compared with the pITP group, the CTD-TP group had a significantly older age of onset and significantly lower leukocyte count, eosinophil count, lymphocyte count, and complement C4 level (P<0.05), as well as significantly higher levels of C-reactive protein, IgE, and IgM (P<0.05). The logistic regression analysis showed that age, IgE, IgM, total B cells, and complement C4 were predictive factors for early differentiation between pITP and CTD-TP (P<0.05). The receiver operating characteristic curve analysis showed that a combination of these five factors had a good discriminative validity, with an area under the curve of 0.944. The correlation analysis showed a negative correlation between IgG and platelet count in the pITP group (r_s=-0.363, P<0.05) and a positive correlation between NK cells and platelet count in the CTD-TP group (r_s=0.713, P<0.05). CONCLUSIONS There is heterogeneity in the clinical and immunological indicators between children with pITP and CTD-TP at initial diagnosis, and these research findings can help with the early differentiation between the two diseases.
Purpura, Thrombocytopenic, Idiopathic/immunology* ; Diagnosis, Differential ; Connective Tissue Diseases/immunology* ; Retrospective Studies ; Early Diagnosis ; Age of Onset ; Leukocyte Count ; Complement C4/immunology* ; C-Reactive Protein/immunology* ; Immunoglobulin E/immunology* ; Immunoglobulin M/immunology* ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; Biomarkers/blood*