Neuroblastoma (NB) is one of the most common tumors in childhood. Unfortunately, the survival outcomes remain unsatisfactory since NB commonly develops multidrug resistance. Recent studies have demonstrated that the high mobility group box 1 (HMGB1)-mediated autophagy promotes chemoresistance in osteosarcoma, lung adenocarcinoma and ovarian cancer, but the exact molecular mechanism underlying HMGB1-mediated autophagy in NB has not been clearly defined. In the present study, we investigated the role of HMGB1 in the development of resistance to anticancer agents in NB. Anticancer agents including doxorubicin, cisplatin and etoposide each induced HMGB1 upregulation, promoted cytosolic HMGB1 translocation and the elevation of autophagic activity in human NB cells. RNA interference-mediated knockdown of HMGB1 restored the chemosensitivity of NB cells. Furthermore, mechanistic investigation revealed that HMGB1 promoted the proliferative activity and invasive potential of NB cells. HMGB1 enhanced drug resistance by inducing Beclin-1-mediated autophagy, an intracellular self-defense mechanism known to confer drug resistance. In addition, we found that HMGB1 facilitated autophagic progression and reduced oxidative stress induced by doxorubicin. Therefore, through its role as a regulator of autophagy, HMGB1 is a critical factor in the development of chemoresistance and tumorigenesis, and it may be a novel target for improving the efficacy of NB therapy.

Objective To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full‐term neonate's risk level of having bacterial meningitis (BM). Methods A multicentered dataset was categorized into derivation (689 full‐term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed. Results In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C‐reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm’s sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%–98.9%) and 98.9% (95% CI 97.6%–99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%–100%) and 98.8% (95% CI 97.7%–100%). Interpretation The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP‐related decision‐making.

Background: Understanding the association of genetic diseases with invasive infections in neonates or infants is important, given the clinical and public health implications of genetic diseases. Methods: We conducted a retrospective case-control study over a 5-year period to investigate the association between genetic diseases and invasive infections in neonates or infants. The case group included 56 patients with laboratory-confirmed invasive infections and a genetic etiology identified by exome sequencing. Another 155 patients without a genetic etiology were selected as controls from the same pool of patients. Results: An overview of genetic diseases that predispose patients to develop invasive infections were outlined. We identified 7 independent predictors for genetic conditions, including prenatal findings [adjusted odds ratio (aOR), 38.44; 95% confidence interval (CI): 3.94–374.92], neonatal intensive care unit admission (aOR, 46.87; 95% CI: 6.30–348.93), invasive ventilation (aOR, 6.66; 95% CI: 3.07–14.46), bacterial infections (aOR, 0.21; 95% CI: 0.06–0.69), fever (aOR, 0.15; 95% CI: 0.08–0.30), anemia (aOR, 6.64; 95% CI: 3.02–14.59) and neutrophilia (aOR, 0.98; 95% CI: 0.96–0.99). The area under the curve for the predictive model was 0.921 (95% CI: 0.876–0.954). We also found that a genetic etiology [hazard ratio (HR), 7.25; 95% CI: 1.71–30.81], neurological manifestations (HR, 3.56; 95% CI: 1.29–9.88) and septic shock (HR, 13.83; 95% CI: 3.18–60.10) were associated with severe outcomes. Conclusions: Our study established predictive variables and risk factors for an underlying genetic etiology and its mortality in neonates or infants with invasive infections. These findings could lead to risk-directed screening and treatment strategies, which may improve patient outcomes.