Utilizing Nearest-Neighbor Clustering for Addressing Imbalanced Datasets in Bioengineering.

Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-Neighbor (OCNN) algorithm. The original inter-quartile range mechanism is replaced with the K-means with outlier removal (KMOR) algorithm for efficient outlier identification in the target class. Parameters are optimized by treating these outliers as non-target-class samples. A new algorithm, the Location-based Nearest-Neighbor (LBNN) algorithm, clusters one-class training data using KMOR and calculates the farthest distance and percentile for each test data point to determine if it belongs to the target class. Experiments cover parameter studies, validation on eight standard imbalanced datasets from KEEL, and three applications on real medical imbalanced datasets. Results show superior performance in precision, recall, and G-means compared to traditional classification models, making it effective for handling imbalanced data challenges.

Association between Air Pollution and Short-Term Outcome of ST-Segment Elevation Myocardial Infarction in a Tropical City, Kaohsiung, Taiwan.

ST-segment elevation myocardial infarction (STEMI), one of the primary factors leading to global mortality, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 µm (PM2.5), <10 µm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between 1 January 2012 and 31 December 2017. Medical records of non-trauma patients aged > 20 years who had presented to the Emergency Department (ED) with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, there was an observed higher risk of in-hospital mortality associated with an increase in the IQR of NO2 during the warm season, specifically in lag 3 (3 days prior to the onset, OR = 3.266; 95%CI: 1.203-8.864, p = 0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI in lag 3 (OR = 2.792; 95%CI: 1.115-6.993, p = 0.028) during the cold season. Our study suggests that exposure to NO2 (during the warm season) and PM10 (during the cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

Salivary Biomarkers to Differentiate between Streptococcus pneumoniae and Influenza A Virus-Related Pneumonia in Children.

Community-acquired pneumonia (CAP) is common among children and can be fatal in certain conditions. In children, CAP can be caused by viral or bacterial infections. Identification of pathogens can help select appropriate therapeutic strategies. Salivary analysis may be a potential diagnostic tool because it is noninvasive, patient-friendly, and easy to perform in children. A prospective study was conducted in children with pneumonia admitted to a hospital. Salivary samples from patients with definite Streptococcus pneumoniae and influenza A strains were used for gel-free (isobaric tag for relative and absolute quantitation (iTRAQ)) proteomics. No statistically significant difference was detected in salivary CRP levels between Streptococcus pneumoniae and influenza A pneumonia in children. Several potential salivary biomarkers were identified using gel-free iTRAQ proteomics to differentiate pneumonia from Streptococcus pneumoniae or influenza A virus infections in pediatric patients. ELISA validated that Streptococcus pneumoniae group has a higher abundance of salivary alpha 1-antichymotrypsin than those in the influenza A group. Whether these salivary biomarkers can be used to distinguish other bacteria from viral pneumonia requires further verification.

Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.

Importance: Early awareness of Kawasaki disease (KD) helps physicians administer appropriate therapy to prevent acquired heart disease in children. However, diagnosing KD is challenging and relies largely on subjective diagnosis criteria. Objective: To develop a prediction model using machine learning with objective parameters to differentiate children with KD from other febrile children. Design, Setting, and Participants: This diagnostic study included 74 641 febrile children younger than 5 years who were recruited from 4 hospitals, including 2 medical centers and 2 regional hospitals, between January 1, 2010, and December 31, 2019. Statistical analysis was performed from October 2021 to February 2023. Main Outcomes and Measures: Demographic data and laboratory values from electronic medical records, including complete blood cell count with differential, urinalysis, and biochemistry, were collected as possible parameters. The primary outcome was whether the febrile children fulfilled the diagnostic criteria of KD. The supervised eXtreme Gradient Boosting (XGBoost) machine learning method was applied to establish a prediction model. The confusion matrix and likelihood ratio were used to evaluate the performance of the prediction model. Results: This study included a total of 1142 patients with KD (mean [SD] age, 1.1 [0.8] years; 687 male patients [60.2%]) and 73 499 febrile children (mean [SD] age, 1.6 [1.4] years; 41 465 male patients [56.4%]) comprising the control group. The KD group was predominantly male (odds ratio, 1.79; 95% CI, 1.55-2.06) with younger age (mean difference, -0.6 years [95% CI, -0.6 to -0.5 years]) compared with the control group. The prediction model's best performance in the testing set was able to achieve 92.5% sensitivity, 97.3% specificity, 34.5% positive predictive value, 99.9% negative predictive value, and a positive likelihood ratio of 34.0, which indicates outstanding performance. The area under the receiver operating characteristic curve of the prediction model was 0.980 (95% CI, 0.974-0.987). Conclusions and Relevance: This diagnostic study suggests that results of objective laboratory tests had the potential to be predictors of KD. Furthermore, these findings suggested that machine learning with XGBoost can help physicians differentiate children with KD from other febrile children in pediatric emergency departments with excellent sensitivity, specificity, and accuracy.

Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma.

Background: The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills. Methods: This is a single-center study of patients admitted to the emergency room from January 2020 to March 2021. We collected 324 patient records for the training model, 36 patient records for validation, and another 36 patient records for testing. We balanced positive and negative Morison's pouch free-fluid detection groups in a 1:1 ratio. The deep learning (DL) model Residual Networks 50-Version 2 (ResNet50-V2) was used for training and validation. Results: The accuracy, sensitivity, and specificity of the model performance for ascites prediction were 0.961, 0.976, and 0.947, respectively, in the validation set and 0.967, 0.985, and 0.913, respectively, in the test set. Regarding feedback prediction, the model correctly classified qualified and non-qualified images with an accuracy of 0.941 in both the validation and test sets. Conclusions: The DL algorithm in ResNet50-V2 is able to detect free fluid in Morison's pouch with high accuracy. The automated feedback and instruction system could help inexperienced sonographers improve their interpretation ability and image acquisition skills.

Effects of Fine Particulate Matter and Its Components on Emergency Room Visits for Pediatric Pneumonia: A Time-Stratified Case-Crossover Study.

Pneumonia, one of the important causes of death in children, may be induced or aggravated by particulate matter (PM). Limited research has examined the association between PM and its constituents and pediatric pneumonia-related emergency department (ED) visits. Measurements of PM2.5, PM10, and four PM2.5 constituents, including elemental carbon (EC), organic carbon (OC), nitrate, and sulfate, were extracted from 2007 to 2010 from one core station and two satellite stations in Kaohsiung City, Taiwan. Furthermore, the medical records of patients under 17 years old who had visited the ED in a medical center and had a diagnosis of pneumonia were collected. We used a time-stratified, case-crossover study design to estimate the effect of PM. The single-pollutant model demonstrated interquartile range increase in PM2.5, PM10, nitrate, OC, and EC on lag 3, which increased the risk of pediatric pneumonia by 18.2% (95% confidence interval (Cl), 8.8-28.4%), 13.1% (95% CI, 5.1-21.7%), 29.7% (95% CI, 16.4-44.5%), 16.8% (95% CI, 4.6-30.4%), and 14.4% (95% Cl, 6.5-22.9%), respectively. After PM2.5, PM10, and OC were adjusted for, nitrate and EC remained significant in two-pollutant models. Subgroup analyses revealed that nitrate had a greater effect on children during the warm season (April to September, interaction p = 0.035). In conclusion, pediatric pneumonia ED visit was related to PM2.5 and its constituents. Moreover, PM2.5 constituents, nitrate and EC, were more closely associated with ED visits for pediatric pneumonia, and children seemed to be more susceptible to nitrate during the warm season.

Machine Learning Models for Survival and Neurological Outcome Prediction of Out-of-Hospital Cardiac Arrest Patients.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes. METHODS: This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014. We applied three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The primary outcome was a favorable neurological outcome at hospital discharge, defined as a Glasgow-Pittsburgh cerebral performance category of 1 to 2. The secondary outcome was a 30-day survival rate and survival-to-discharge rate. RESULTS: The final analysis included 1071 participants from the study period. For neurologic outcome prediction, the area under the receiver operating curve (AUC) was 0.819, 0.771, and 0.956 in LR, SVM, and XGB, respectively. The sensitivity and specificity were 0.875 and 0.751 in LR, 0.687 and 0.793 in SVM, and 0.875 and 0.904 in XGB. The AUC was 0.766 and 0.732 in LR, 0.749 and 0.725 in SVM, and 0.866 and 0.831 in XGB, for survival-to-discharge and 30-day survival, respectively. CONCLUSIONS: Prognostic models trained with ML technique showed appropriate calibration and high discrimination for survival and neurologic outcome of OHCA without using prehospital data, with XGB exhibiting the best performance.

Fine particulate matter constituents associated with emergency room visits for pediatric asthma: a time-stratified case-crossover study in an urban area.

BACKGROUND: Global asthma-related mortality tallies at around 2.5 million annually. Although asthma may be triggered or exacerbated by particulate matter (PM) exposure, studies investigating the relationship of PM and its components with emergency department (ED) visits for pediatric asthma are limited. This study aimed to estimate the impact of short-term exposure to PM constituents on ED visits for pediatric asthma. METHODS: We retrospectively evaluated non-trauma patients aged younger than 17 years who visited the ED with a primary diagnosis of asthma. Further, measurements of PM with aerodynamic diameter of < 10 µm (PM10), PM with aerodynamic diameter of < 10 µm (PM2.5), and four PM2.5 components (i.e., nitrate (NO3-), sulfate (SO42-), organic carbon (OC), and elemental carbon (EC)) were collected between 2007 and 2010 from southern particulate matter supersites. These included one core station and two satellite stations in Kaohsiung City, Taiwan. A time-stratified case-crossover study was conducted to analyze the hazard effect of PM. RESULTS: Overall, 1597 patients were enrolled in our study. In the single-pollutant model, the estimated risk increase for pediatric asthma incidence on lag 3 were 14.7% [95% confidence interval (CI), 3.2-27.4%], 13.5% (95% CI, 3.3-24.6%), 14.8% (95% CI, 2.5-28.6%), and 19.8% (95% CI, 7.6-33.3%) per interquartile range increments in PM2.5, PM10, nitrate, and OC, respectively. In the two-pollutant models, OC remained significant after adjusting for PM2.5, PM10, and nitrate. During subgroup analysis, children were more vulnerable to PM2.5 and OC during cold days (< 26 °C, interaction p = 0.008 and 0.012, respectively). CONCLUSIONS: Both PM2.5 concentrations and its chemical constituents OC and nitrate are associated with ED visits for pediatric asthma. Among PM2.5 constituents, OC was most closely related to ED visits for pediatric asthma, and children are more vulnerable to PM2.5 and OC during cold days.

Epidemiology and clinical manifestations of children with macrolide-resistant Mycoplasma pneumoniae pneumonia in Southern Taiwan.

BACKGROUND: Mycoplasma pneumoniae is a pneumonia-causing pathogen commonly found in pediatric patients in Taiwan. Recently, macrolide-resistant (MR) strains have been emerging globally. The prevalence of pneumonia due to MR-M. pneumoniae (hereafter, MPP) in northern Taiwan before 2017 has been reported to be 12.3-24%. The prevalence of MR-MPP within a specific location can vary. Hence, we investigated the prevalence of MR-MPP in southern Taiwan. METHODS: Eighty-one children with PCR-confirmed MPP were enrolled between July 2016 and June 2019. They were assigned to macrolide-sensitive (MS) and MR groups based on their PCR results, and their clinical manifestations and laboratory data were compared. RESULTS: The proportions of patients with MS-MPP and MR-MPP varied with time. The average ratio of the proportion of MR-MPP was 54.3% in this study. Patients with MR-MPP had lower neutrophil counts, higher lymphocyte counts, and higher platelet counts than those with MS-MPP. In contrast with the 40% of the MR-MPP group that still had a fever after three days of azithromycin treatment, only 11.8% of the MS-MPP group still had a fever. CONCLUSION: Our study provided valuable epidemiological survey information for children with MR-MPP in southern Taiwan. The prevalence of MR-MPP was different from that reported in previous studies in northern Taiwan. Specific MR strains should be considered in children with MPP if they still have a fever after three days of macrolide treatment.

Short-Term Effects of Particulate Matter and Its Constituents on Emergency Room Visits for Chronic Obstructive Pulmonary Disease: A Time-Stratified Case-Crossover Study in an Urban Area.

BACKGROUND: PM2.5 exposure is associated with pulmonary and airway inflammation, and the health impact might vary by PM2.5 constitutes. This study evaluated the effects of increased short-term exposure to PM2.5 constituents on chronic obstructive pulmonary disease (COPD)-related emergency department (ED) visits and determined the susceptible groups. METHODS: This retrospective observational study performed in a medical center from 2007 to 2010, and enrolled non-trauma patients aged >20 years who visited the emergency department (ED) and were diagnosed as COPD. Concentrations of PM2.5, PM10, and the four PM2.5 components, including organic carbon (OC), elemental carbon (EC), nitrate (NO3-), and sulfate (SO42-), were collected by three PM supersites in Kaohsiung City. We used an alternative design of the Poisson time series regression models called a time-stratified and case-crossover design to analyze the data. RESULTS: Per interquartile range (IQR) increment in PM2.5 level on lag 2 were associated with increments of 6.6% (95% confidence interval (CI), 0.5-13.0%) in risk of COPD exacerbation. An IQR increase in elemental carbon (EC) was significantly associated with an increment of 3.0% (95% CI, 0.1-5.9%) in risk of COPD exacerbation on lag 0. Meanwhile, an IQR increase in sulfate, nitrate, and OC levels was not significantly associated with COPD. Patients were more sensitive to the harmful effects of EC on COPD during the warm season (interaction p = 0.019). The risk of COPD exacerbation after exposure to PM2.5 was higher in individuals who are currently smoking, with malignancy, or during cold season, but the differences did not achieve statistical significance. CONCLUSION: PM2.5 and EC may play an important role in COPD events in Kaohsiung, Taiwan. Patients were more susceptible to the adverse effects of EC on COPD on warm days.

A novel score system of blood tests for differentiating Kawasaki disease from febrile children.

BACKGROUND: Kawasaki disease is the most common cause of acquired heart disease among febrile children under the age of 5 years old. It is also a clinically diagnosed disease. In this study, we developed and assessed a novel score system using objective parameters to differentiate Kawasaki disease from febrile children. METHODS: We analyzed 6,310 febrile children and 485 Kawasaki disease subjects in this study. We collected biological parameters of a routine blood test, including complete blood count with differential, C-reactive protein, aspartate aminotransferase, and alanine aminotransferase. Receiver operating characteristic curve, logistic regression, and Youden's index were all used to develop the prediction model. Two other independent cohorts from different hospitals were used for verification. RESULTS: We obtained eight independent predictors (platelets, eosinophil, alanine aminotransferase, C-reactive protein, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and monocyte) and found the top three scores to be eosinophil >1.5% (score: 7), alanine aminotransferase >30 U/L (score: 6), and C-reactive protein>25 mg/L (score: 6). A score of 14 represents the best sensitivity value plus specificity prediction rate for Kawasaki disease. The sensitivity, specificity, and accuracy for our cohort were 0.824, 0.839, and 0.838, respectively. The verification test of two independent cohorts of Kawasaki disease patients (N = 103 and 170) from two different institutes had a sensitivity of 0.780 (213/273). CONCLUSION: Our findings demonstrate a novel score system with good discriminatory ability for differentiating between children with Kawasaki disease and other febrile children, as well as highlight the importance of eosinophil in Kawasaki disease. Using this novel score system can help first-line physicians diagnose and then treat Kawasaki disease early.

Altered chemokine profile in Refractory Mycoplasma pneumoniae pneumonia infected children.

BACKGROUND: Mycoplasma pneumoniae is one of the major pathogens causing community-acquired pneumonia in children. Although usually self-limited, Mycoplasma pneumoniae pneumonia (MPP) may lead to complicated morbidity that can even be life-threatening. Upon MPP infection, alveolar macrophage becomes attracted and activated and will induce subsequent cytokine and chemokine reaction. Refractory Mycoplasma pneumoniae pneumonia (RMPP) is manifested by clinical or radiological deterioration despite proper antibiotic therapy. RMPP is characterized with excessive inflammation and may need subsequent glucocorticoid treatment. AIM: The aim of this study was to investigate the change of plasma chemokines in non-refractory Mycoplasma pneumoniae pneumonia (NRMPP) and RMPP before and after antibiotic or methylprednisolone treatment. METHOD: A total of 42 children with MPP were enrolled in this study. Plasma specimens were collected at admission and one to two weeks after antibiotic or methylprednisolone treatment with declined fever. Plasma specimens were then indicated to chemokines detection. RESULTS: Mycoplasma pneumoniae pneumonia altered the chemokine profile through the observation of decreased plasma M1 related chemokines (CCL2, CCL8 and CXCL10) and increased M2 related chemokines (CCL17 and CCL22) after treatment.When the patients were divided into RMPP and NRMPP groups and the chemokines before treatment were compared, the RMPP group showed higher CXCL10 but lower CCL3 and CCL11 than the NRMPP group. CONCLUSION: Unique changes in macrophage related chemokines is observed in the course of MPP infection. NRMPP and RMPP infection in children showed distinct manifestation in chemokine profiles.

Higher Hospitalization Rate for Lower Airway Infection in Transfusion-Naïve Thalassemia Children.

Few studies have addressed the risk of infection in transfusion-naïve thalassemia patients. We aimed to investigate whether transfusion-naïve thalassemia population has higher hospitalization rates for lower airway infection-related diseases than non-thalassemia population in children. A nationwide population-based retrospective cohort study was conducted using detailed medical records of the Taiwan National Health Insurance Research Database. Transfusion-naïve thalassemia patients were compared with a matched cohort at a ratio of 1:4. Data of the selected patients were adjusted for age, sex, and related comorbidities. We recorded the frequency of admissions or outpatient clinic visits for patients with a diagnosis of pneumonia or acute bronchitis/bronchiolitis. Based on our results, the hospitalization rates and incidence rate ratios of bronchitis/bronchiolitis and pneumonia for transfusion-naïve thalassemia children were all higher than those for non-thalassemia controls. Therefore, we conclude that transfusion-naïve thalassemia children are more likely to experience lower airway infections and have a higher probability of hospitalization for these conditions.

C-Reactive Protein to Albumin Ratio for Predicting Coronary Artery Lesions and Intravenous Immunoglobulin Resistance in Kawasaki Disease.

Background: C-reactive protein (CRP) to albumin ratio (CRP/Alb) has emerged as a novel marker of inflammation, but few studies have evaluated the role of CRP/Alb ratio in Kawasaki disease (KD). Coronary artery lesions (CAL) in children with KD is a major acquired heart disease. We aimed to assess the association between CRP/Alb ratio and CAL formation in children with KD. Methods: This retrospective study enrolled children diagnosed with KD and divided them into two groups, KD with CAL and KD without CAL. We compared the difference in gender, age, laboratory data, intravenous immunoglobulin (IVIG) resistance rate, and incidence of CAL between the two groups. Multivariable logistic regression analysis was used to assess the independent risk factors of CAL. We adopted receiver operating characteristic (ROC) curve analysis to determine the predictive ability of CRP/Alb ratio in predicting CAL. Results: In total, 410 KD patients were reviewed, with 143 in the KD with CAL group and 267 in the KD without CAL group. KD children with CAL had a higher CRP/Alb ratio than those without CAL (3.14 ± 3.17 vs. 2.12 ± 2.04, p < 0.001). Multivariable logistic regression analysis demonstrated that male gender (OR = 3.222, p < 0.001), incomplete KD (OR = 1.968, p = 0.031), greater platelet count (OR = 1.004, p < 0.001), higher CRP (OR = 0.982, p = 0.048), and higher CRP/Alb ratio (OR = 1.994, P = 0.016) were all independent risk factors for predicting CAL. KD children with a high CRP/Alb ratio (≥2.94) had a higher incidence rate of CAL and IVIG resistance than those with a low CRP/Alb ratio (<2.94) (49.6 vs. 28.7%, p < 0.001 and 11.6 vs. 3.5%, p = 0.001, respectively). Conclusions: This report is the first to show the role of CRP/Alb ratio in KD children. CRP/Alb ratio can serve as a novel predicting marker for CAL formation and IVIG resistance in KD.

Diagnosis of common pulmonary diseases in children by X-ray images and deep learning.

Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.

Use of saliva sample to detect C-reactive protein in children with pneumonia.

BACKGROUND: Serum C-reactive protein (CRP) is a sensitive biomarker for inflammation and is broadly used to clinically diagnose infectious diseases, including pneumonia. However, blood sampling is fraught with technical difficulties in children. The salivary analysis may be a potential diagnostic tool, as it is noninvasive, patient-friendly, and easy to perform in children. This study aimed to evaluate the use of salivary CRP as a biomarker for children with pneumonia. METHODS: A prospective study was conducted in patients aged 2 to 17 years, admitted to the hospital with pneumonia. Saliva and serum samples for CRP and chemokine determination were collected at the initial admission and during a follow-up from pediatric patients with pneumonia. Salivary samples were also collected from healthy subjects used as controls. RESULTS: A total of 60 healthy children and 106 pediatric patients with pneumonia were enrolled in this study. The salivary CRP level was much higher in pediatric patients with pneumonia than in healthy children (48.77 ± 5.52 ng/mL vs 14.78 ± 3.92 ng/mL; P < .001). Salivary CRP level was highly correlated with serum CRP level in pediatric patients with pneumonia (r = .679; P < .001). Salivary CRP level (≥40.307 ng/mL) can be used to predict high serum CRP levels (≥80 mg/L) with an area under the curve of 0.810 (95% confidence interval, 0.740-0.881). As pneumonia improved, both salivary and serum CRP levels decreased during follow-up. CONCLUSIONS: Salivary CRP could be an alternative biomarker for serum CRP in pediatric patients with pneumonia. This is especially beneficial for pediatric patients, as saliva collection is simple, noninvasive, and patient-friendly.

A Multifactorial Evaluation of the Effects of Air Pollution and Meteorological Factors on Asthma Exacerbation.

In the real world, dynamic changes in air pollutants and meteorological factors coexist simultaneously. Studies identifying the effects of individual pollutants on acute exacerbation (AE) of asthma may overlook the health effects of the overall combination. A comprehensive study examining the influence of air pollution and meteorological factors is required. Asthma AE data from emergency room visits were collected from the Taiwan National Health Insurance Research Database. Complete monitoring data for air pollutants (SO2; NO2; O3; CO; PM2.5; PM10) and meteorological factors were collected from the Environmental Protection Agency monitoring stations. A bi-directional case-crossover analysis was used to investigate the effects of air pollution and meteorological factors on asthma AE. Among age group divisions, a 1 °C temperature increase was a protective factor for asthma ER visits with OR = 0.981 (95% CI, 0.971-0.991) and 0.985 (95% CI, 0.975-0.994) for pediatric and adult patients, respectively. Children, especially younger females, are more susceptible to asthma AE due to the effects of outdoor air pollution than adults. Meteorological factors are important modulators for asthma AE in both asthmatic children and adults. When studying the effects of air pollution on asthma AE, meteorological factors should be considered.

Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department.

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

Serum lactate dehydrogenase isoenzymes 4 plus 5 is a better biomarker than total lactate dehydrogenase for refractory Mycoplasma pneumoniae pneumonia in children.

Although usually self-limiting, Mycoplasma pneumoniae pneumonia (MPP) may lead to clinical or radiological deterioration despite macrolide antibiotic therapy, resulting in the development of refractory MPP (RMPP). Corticosteroids have been used to treat RMPP with beneficial effects. Serum lactate dehydrogenase (LDH) is a suggested biomarker for the use of steroid therapy. Since serum LDH is a non-specific marker and elevated in many inflammatory processes, this study investigates the predicting level of LDH isoenzymes for RMPP. Fifty-four children with non-refractory M. pneumoniae pneumonia (NRMPP) and 16 children with RMPP were enrolled in this study. In comparison to the NRMPP group, the RMPP group showed significantly higher levels of serum LDH. Concerning LDH isoenzymes, the RMPP group showed significantly lower proportions of LDH1 and LDH2, while higher LDH4 and LDH5 percentage. Receiver operating characteristic curve analysis showed that the area under the curve for the total LDH data was 0.812 with a cut-off of 408 IU/L (sensitivity of 75.0%, specificity of 72.2%). The areas under the curve for LDH4, LDH5, and [LDH4 + LDH5] were estimated to be 0.813, 0.818, and 0.829, respectively. The threshold for [LDH4 + LDH5] was estimated to be 109.4 IU/L (sensitivity, 75.0%; specificity, 87.0%). These results indicate that for the initiation of corticosteroid therapy, serum [LDH4 + LDH5] level is a more sensitive biomarker than total LDH.

L-Arginine and L-Citrulline Supplementation Have Different Programming Effect on Regulatory T-Cells Function of Infantile Rats.

Arginine is a semiessential amino acid in healthy adult human, but is essential for preterm, newborn or critically ill patients. Arginine can be supplied from our diet or de novo synthesis from citrulline. In conditions of sepsis or endotoxemia, arginine may be deficient and be accompanied with altered immune response. L-arginine supplementation can ameliorate dysregulated immune condition and improve prognosis. Many studies had tried L-arginine or L-citrulline supplementation to examine the effect on immune response in the adult population. Few had studied on the young children. In this study, we determined the effect of L-arginine and L-citrulline supplementation on the immune response of infantile rats. Male infantile rats received normal saline, L-arginine (200 mg/kg/day) or L-citrulline (200 mg/kg/day) intraperitoneally over postnatal day 8 to day 14. The infantile rats were then sacrificed. The blood was analyzed while the spleen was indicated for immune analysis after stimulation with concanavalin A (Con A) or lipopolysaccharide (LPS). We found L-arginine supplementation enhanced Th1 immune response by increasing IFN-γ production. Both the L-arginine and L-citrulline therapy can modulate regulatory T-cell (Treg) immune effects by increasing the IL-10 level. Only the L-citrulline group showed a TGF-ß1 increase. Both L-arginine and L-citrulline therapy were also noted to decrease SMAD7 expression and enhance SIRT-1 abundance. However, FOXP3 expression was only modulated by L-citrulline treatment. We then concluded that L-arginine and L-citrulline supplementation can modulate the regulatory T-cells function differently for infantile rats.