Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables

Metabolomic analysis of the serum and urine of rats exposed to diazinon, dimethoate, and cypermethrin alone or in combination

BACKGROUND

Multiple pesticides are often used in combination for plant protection and public health. Therefore, it is important to analyze the physiological changes induced by multiple pesticides exposure. The objective of this study was to investigate the combined toxicity of the widely-used organophosphorus and pyrethroid pesticides diazinon, dimethoate, and cypermethrin.

METHODS

Male Wistar rats were administrated by gavage once daily with the three pesticides individual or in combination for consecutive 28 days. The metabolic components of serum and urine samples were detected by using 1H nuclear magnetic resonance (NMR)-based metabolomics method. Histopathological examination of liver and kidneys and serum biochemical determination were also carried out.

RESULTS

The results showed that after the 28-day subacute exposure, serum glutamic transaminase and albumin were significantly increased and blood urea nitrogen was significantly decreased in the rats exposed to the mixture of the pesticides compared with the control rats, suggesting that the co-exposure impaired liver and kidney function. Metabolomics analysis indicated that the indicators 14 metabolites were statistically significant altered in the rats after the exposure of the pesticides. The increase in 3-hydroxybutyric acid in urine or decrease of lactate and N-acetyl-L-cysteine in serum could be a potentially sensitive biomarker of the subchronic combined effects of the three insecticides. The reduction level of 2-oxoglutarate and creatinine in urine may be indicative of dysfunction of liver and kidneys.

CONCLUSION

In summary, the exposure of rats to pesticides diazinon, dimethoate, and cypermethrin could cause disorder of lipid and amino acid metabolism, induction of oxidative stress, and dysfunction of liver and kidneys, which contributes to the understanding of combined toxic effects of the pesticides revealed by using the metabolomics analysis of the urine and serum profiles.

Artificial intelligence differentiates abdominal Henoch-Schönlein purpura from acute appendicitis in children

OBJECTIVE

This study aims to construct an artificial intelligence (AI) model capable of effectively discriminating between abdominal Henoch-Schönlein purpura (AHSP) and acute appendicitis (AA) in pediatric patients.

METHODS

A total of 6965 participants, comprising 2201 individuals with AHSP and 4764 patients with AA, were enrolled in the study. Additionally, 53 laboratory indicators were taken into consideration. Five distinct artificial intelligence (AI) models were developed employing machine learning algorithms, namely XGBoost, AdaBoost, Gaussian Naïve Bayes (GNB), MLPClassifier (MLP), and support vector machine (SVM). The performance of these prediction models was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve assessment, and decision curve analysis (DCA).

RESULTS

We identified 32 discriminative indicators (p < .05) between AHSP and AA. Five indicators, namely the lymphocyte ratio (LYMPH ratio), eosinophil ratio (EO ratio), eosinophil count (EO count), neutrophil ratio (NEUT ratio), and C-reactive protein (CRP), exhibited strong performance in distinguishing AHSP from AA (AUC ≥ 0.80). Among the various prediction models, the XGBoost model displayed superior performance evidenced by the highest AUC (XGBoost = 0.895, other models < 0.89), accuracy (XGBoost = 0.824, other models < 0.81), and Kappa value (XGBoost = 0.621, other models < 0.60) in the validation set. After optimization, the XGBoost model demonstrated remarkable diagnostic performance for AHSP and AA (AUC > 0.95). Both the calibration curve and decision curve analysis suggested the promising clinical utility and net benefits of the XGBoost model.

CONCLUSION

The AI-based machine learning model exhibits high prediction accuracy and can differentiate AHSP and AA from a data-driven perspective.

Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics

BACKGROUND

Pulmonary arterial hypertension is a common complication in patients with congenital heart disease. In the absence of early diagnosis and treatment, pediatric patients with PAH has a poor survival rate. Here, we explore serum biomarkers for distinguishing children with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) from CHD.

METHODS

Samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and 22 metabolites were further quantified by ultra-high-performance liquid chromatography-tandem mass spectroscopy.

RESULTS

Serum levels of betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered between CHD and PAH-CHD. Logistic regression analysis showed that combination of serum SAM, guanine and N-terminal pro-brain natriuretic peptide (NT-proBNP), yielded the predictive accuracy of 157 cases was 92.70% with area under the curve of the receiver operating characteristic curve value of 0.9455.

CONCLUSION

We demonstrated that a panel of serum SAM, guanine and NT-proBNP is potential serum biomarkers for screening PAH-CHD from CHD.

Metabolomics analysis reveals amelioration effects of yellowhorn tea extract on hyperlipidemia, inflammation, and oxidative stress in high-fat diet-fed mice

Yellowhorn tea (YT) is traditionally used as a lipid-lowering beverage in Mongolian minorities. However, the pharmacological effects of YT extract and its specific metabolic changes in hyperlipidemia models are not fully understood. The aim of this study was to identify biomarkers using untargeted metabolomics techniques and to investigate the mechanisms underlying the changes in metabolic pathways associated with lipid lowering, anti-inflammation and anti-oxidant in hyperlipidemic mice. A high-fat diet (HFD)-induced hyperlipidemic mouse model was established. YT extract was administered as oral gavage at 0.15, 0.3, and 0.6 g/kg doses for 10 weeks. HFD-induced hyperlipidemia and the therapeutic effect of YT extract were evaluated based on histopathology and by assessing blood lipid levels. Liver inflammatory factors and oxidative stress indices were determined using enzyme-linked immunosorbent assays. Liver metabolites were evaluated using untargeted metabolomics. Biochemical and histological examinations showed that YT extract significantly reduced body-weight gain (p < 0.01) and fat deposition in tissues. YT extract significantly reduced the levels of serum and liver triglyceride and total cholesterol; inflammatory factors [interleukin (IL)-6, IL-1β, and tumor necrosis factor-α]; malondialdehyde; and leptin (p < 0.05) in hyperlipidemic mice. YT extract also significantly increased the levels of oxidative stress indicators (superoxide dismutase, catalase, and glutathione peroxidase) and adiponectin. Metabolomics studies revealed several endogenous molecules were altered by the high-fat diet and recovery following intervention with YT extract. The metabolites that were significantly different in the liver after YT intake included citicoline, acetylcholine, pyridoxine, and NAD. Pathway analysis indicated that YT extract ameliorated HFD-induced hyperlipidemia in mice via three major metabolic pathways, namely, glycerophospholipid metabolism, vitamin B6 metabolism, and nicotinate and nicotinamide metabolism. This study demonstrates YT extract has profound effects on the alleviation of HFD-induced hyperlipidemia, inflammation and oxidative stress.

Novel molecular biomarkers and diagnosis of acute appendicitis in children

Reliable and efficient diagnosis of pediatric appendicitis is essential for the establishment of a clinical management plan and improvement of patient outcomes. Current strategies used to diagnose a child presenting with a suspected appendicitis include laboratory studies, clinical scores and diagnostic imaging. Although these modalities work in conjunction with each other, one optimal diagnostic strategy has yet to be agreed upon. The recent introduction of precision medicine techniques such as genomics, transcriptomics, proteomics and metabolomics has increased both the diagnostic sensitivity and specificity of appendicitis. Using these novel strategies, the integration of precision medicine into clinical practice via point-of-care technologies is a plausible future. These technologies would assist in the screening, diagnosis and prognosis of pediatric appendicitis.

TLR4 Knockout Attenuates BDL-induced Liver Cholestatic Injury through Amino Acid and Choline Metabolic Pathways

The exact mechanism by which knockout of Toll-like receptor 4 (TLR4) attenuates the liver injury remains unclear. The present study aimed to examine the role of TLR4 in the pathogenesis of bile duct ligation (BDL)-induced liver cholestatic injury and the underlying mechanism. Wild type (WT) mice and TLR4 knockout (TLR4-KO) mice were used for the establishment of the BDL model. Metabolomics were applied to analyze the changes of small molecular metabolites in the serum and liver of the two groups. The serum biochemical indexes and the HE staining results of liver tissue showed that liver damage was significantly reduced in TLR4-KO mice after BDL when compared with that in WT mice. The metabolite analysis results showed that TLR4 KO could maintain the metabolisms of amino acids- and choline-related metabolites. After BDL, the amino acids- and choline-related metabolites, especially choline and 3-hydroxybutyrate, were significantly increased in WT mice (both in serum and liver), but these metabolites in the liver of TLR4-KO mice after BLD were not significant different from those before BLD. In conclusion, TLR4 KO could attenuate BDL-induced liver cholestatic injury through regulating amino acid and choline metabolic pathways.

Inflammatory Markers and Duration of Symptoms Have a Close Connection With Diagnosis and Staging of Acute Appendicitis in Children

Background: For children with acute appendicitis (AA), a clear diagnosis is a challenge. The purpose of this study is to explore whether inflammatory markers in the blood combined with symptom duration are helpful in the diagnosis of acute appendicitis and in predicting the severity of acute appendicitis. Methods: All the selected patients underwent appendectomy between November 10, 2011 and November 15, 2019, in whom preoperative WBCC, CRP, and NE% had been measured in a short time. All patients were divided into two groups: uncomplicated AA and complicated AA, postoperatively. Results: For our standards, 813 patients were selected, 442 of them had complicated AA. The mean [standard deviation (SD)] age for the uncomplicated AA group was 9.78 ± 2.02 years and for the complicated AA group was 9.69 ± 2.16 years (P = 0.55). Elevated WBCC, CRP, and NE% had a higher relatively sensitivity in complicated AA than uncomplicated AA especially when WBCC, CRP, and NE% were at normal levels, which had a sensitivity of 100% in uncomplicated AA, but this only applied to nine patients. CRP values were significantly different in three time groups, whether uncomplicated or complicated AA. Conclusion: The combination of WBCC, CRP, and NE% values is very sensitive for the diagnosis of acute appendicitis, and when we predict complicated AA using the CRP value, we also need to consider the time of symptom onset.

Predicting the Severity of Acute Appendicitis of Young Children (<3 Years Old): Development and Assessment of a New Prediction Nomogram

Objective: There is a lack of assessment methods of acute appendicitis in little children. The purpose of this study was to develop and internally validate a nomogram for predicting the severity of acute appendicitis of young children (<3 years old). Methods: We develop a prediction model based on a training dataset of 121 patients (<3 years old) with acute appendicitis. Admission information was collected between January 2010 and January 2021, which contained demographic characteristic, laboratory examinations, treatment and pathology type, etc. Logistic regression analysis was used to identify independent risk factors and establish the predictive model. C-index and calibration curves were applied to evaluate the performance of the nomogram. Then corrected C-index was calculated to conduct internal verification by using the bootstrapping validation. Decision curve analysis determined clinical application of the prediction model. Results: Predictors contained in the prediction nomogram included weight for age, onset time (from developing symptoms to hospital), admission temperature, leukocyte count, neutrophil ratio, and total bilirubin. Logistic regression analysis showed that weight for age (X1) < -2.32 SD (P = 0.046), onset time (X2) > 2.5 days (P = 0.044), admission temperature (X3) > 38.5°C (P = 0.009), leukocyte count (X4) > 12.185^*109/L (P = 0.045), neutrophil ratio (X5) > 68.7% (P = 0.029), and total bilirubin (X6) > 9.05 μmol/L (P = 0.035) were found to be significant for predicting the severity of appendicitis. The logistic regression equation was logit (P) = -0.149X1 + 0.51X2 + 1.734X3 + 0.238X4 + 0.061X5 + 0.098X6 - 75.229. C-index of nomogram was calculated at 0.8948 (95% Cl: 0.8332-0.9567) and it still was 0.8867 through bootstrapping validation. Decision curve analysis showed that when the threshold probability ranged from 14 to 88%, there is a net benefit of using this prediction model for severity of appendicitis in little children. Conclusion: This novel nomogram incorporating the weight for age, onset time, admission temperature, leukocyte count, neutrophil ratio, and total bilirubin could be conveniently used to estimate the severity of appendicitis of young children <3 years old) and determine appropriate treatment options in time.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Metabonomics of d-glucaro-1,4-lactone in preventing diethylnitrosamine-induced liver cancer in rats

CONTEXT

d-Glucaro-1,4-lactone (1,4-GL) exists in many vegetables and fruits. Metabonomics has not been used to investigate the role of 1,4-GL in preventing liver cancer.

OBJECTIVE

The pharmacological effects and metabolite alterations of 1,4-GL on the prevention of diethylnitrosamine (DEN)-induced liver cancer were investigated.

MATERIALS AND METHODS

Ten healthy Sprague-Dawley rats served as control and 46 were used to establish rat liver cancer model. ¹HNMR-based metabonomics was used to compare the effects of oral 1,4-GL (50 mg/kg) in liver cancer rats (n = 26) after 10 consecutive weeks of intervention. The amino acids in rat serum were quantified by HPLC-UV, and the changes in Fischer's ratio were calculated.

RESULTS

The 20-week survival rate of DEN-induced liver cancer rats administered with oral 1,4-GL was increased from 45.0 to 70.0% with reduced carcinogenesis of the liver and significantly lowered serum α-fetoprotein level (14.28 ± 2.89 ng/mL vs. 18.56 ± 4.65 ng/mL, p = 0.012). The serum levels of leucine, valine, 3-hydroxybutyrate, lactate, acetate and glutamine in the DEN + 1,4-GL group returned to normal levels compared with those of the DEN group on week 20. Fischer's ratio in the rat serum of DEN group was 1.62 ± 0.21, which was significantly lower than that in healthy rats (2.3 ± 0.12). However, Fischer's ratio increased to 1.89 ± 0.22 in the DEN + 1,4-GL group.

DISCUSSION AND CONCLUSIONS

1,4-GL exerted positive effects on liver carcinogenesis in rats by pathological examination and metabonomic analysis. Its mechanism may be related to the restoration of amino acid and energy metabolism.

Discovery and Validation of Potential Serum Biomarkers for Pediatric Patients with Congenital Heart Diseases by Metabolomics

To identify and screen serum biomarkers to determine pediatric patients with congenital heart diseases (PCH) from healthy control children (NC), a total of 614 clinically diagnosed subjects from three hospitals, including 491 PCH and 234 NC, were enrolled for nontargeted proton nuclear magnetic resonance spectroscopy (¹H NMR)-based and targeted ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS)-based metabolomics studies. Nineteen serum metabolites distinguishing PCH from NC were identified by ¹H NMR-based metabolomic analysis. The amino acid and choline metabolic pathways were considered to be closely related to PCH. The serum levels of 13 metabolites in these two pathways were further determined by UPLC-MS/MS and observed to be altered significantly in PCH. Taurine, glutamine, and glutamate presented considerable diagnostic value for the diagnosis of PCH (AUROC > 0.80). Logistic regression analysis showed that a combination of four variables, namely, betaine, taurine, glutamine, and phenylalanine, yields a high diagnostic value (AUROC = 0.949) and prediction accuracy (89.1%) for differentiating PCH from the NC, and the sensitivity and specificity were 93.9 and 95.2%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 83.8% for 80 unknown samples. Our results showed that the serum amino acid and choline metabolite levels in PCH were changed considerably. The combination of four metabolites, namely, betaine, taurine, glutamine, and phenylalanine, can be used as potential serum biomarkers in PCH diagnosis, which contributes to the early PCH screening.