Background and aimsDeciding when to suspect hemophagocytic lymphohistiocytosis (HLH) and perform diagnostic tests in patients with acute infection of Epstein-Barr virus (EBV) is challenging, given the high prevalence of EBV infection, the life-threatening risk of EBV-HLH, the relatively low incidence of EBV-HLH, and the wide spectrum of disease presentations. The aim of this study was to develop an EBV-HLH screening model for pediatric patients diagnosed with acute infection of EBV.MethodsAn inpatient cohort with 3183 pediatric patients who were diagnosed with active infection of EBV was used to construct and validate the EBV-HLH screening score model. The model parameters were selected from common laboratory parameters using the method of Akaike Information Criterion-optimal selection through cross-validation under logistic regression. Performance of the score was evaluated and compared with the performance of screening methods using the number of cytopenias lineages.ResultsThe EBV-HLH screening score has five parameters, including hemoglobin, platelet, neutrophil, albumin, and lactate dehydrogenase. Using a cut-of value of 29, the scoring model had a sensitivity of 89.2% and a specificity of 89.5% in the validation set. The false negative rate, false positive rate, positive predictive value, and negative predictive value in the validation set was 10.8%, 10.5%, 26.8%, and 99.5%, respectively, similar to that of the training set.ConclusionsWith five common laboratory parameters, the EBV-HLH score provides a simple tool to assist the identification of EBV patients who require further evaluation of HLH. Further studies are needed to evaluate the generalizability of the score and optimize the diagnose process for EBV-HLH.

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As the scale of the distribution network continues to increase, the importance of distribution network is getting higher and higher. Therefore, it is necessary to achieve fault location and restore it as soon as possible after the fault occurs. This paper proposes a new fault location algorithm for the radial distribution network, which is improved on the basis of traditional matrix algorithm. First, the power failure incidence matrix (PFIM) and power interruption information matrix (PIIM) is constructed based on the topological connection and fault information at each node. The fault location matrix (FLM) is then obtained through PFLM and PIIM to realize the fault location. Second, the accuracy of the proposed algorithm is verified by mathematical derivation. Finally, an 11-node radial distribution network is illustrated to testify the proposed algorithm. Results show that the improved fault location matrix algorithm proposed in this paper can effectively achieve fault location in radial distribution network.

Background Hemophagocytic lymphohistiocytosis (HLH) is a rare but life-threatening disease with rapid progressing and high mortality, which is more commonly seen in children. Objective Our goal was to develop a novel model for predicting early mortality risk in pediatric HLH patients using readily accessible parameters and build a nomogram. Methods We conducted a retrospective analysis of 170 pediatric HLH patients diagnosed at Hunan Children's Hospital between March 1, 2017, and March 1, 2022. These patients were split into a training cohort and a validation cohort. Early mortality was defined as 28-day mortality post-diagnosis. A prediction model with nomogram was developed using binary logistic regression analysis in the training cohort. The model underwent internal and external validation using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results The final prediction model included 11 predictor variables: glutamic-pyruvic transaminase, albumin, globulin, myohemoglobin, creatine kinase, serum potassium, procalcitonin, serum ferritin, the interval between onset and diagnosis, and the interval between admission and diagnosis. The 28-day mortality prediction AUC was 0.957 in the training cohort and 0.929 in the validation cohort. Utilizing the 28-day mortality prediction for estimating 7-day and 14-day mortality, the AUC values were 0.930 and 0.938, respectively. The calibration plot revealed an adequate fit with 1000 bootstrap resampling and the DCA exhibited great net benefit. Conclusion The study constructed a novel prediction model with nomogram in pediatric HLH, which could contribute to rapid assessment early mortality risk after diagnosis with readily available parameters, providing clinical support to identify patients with a poor prognosis and enhancing their prognostic outcomes.

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