The neutrophil extracellular traps in neurological diseases: An update.

Neutrophil extracellular traps released by neutrophils are web-like DNA structures adhered to granulin proteins with bactericidal activity and can be an important mechanism for preventing pathogen dissemination or eliminating microorganisms. However, they also play important roles in diseases of other systems, such as the central nervous system. We tracked the latest advances and performed a review based on published original and review articles related to neutrophil extracellular traps and neurological diseases. Generally, neutrophils barely penetrate the blood-brain barrier into the brain parenchyma, but when pathological changes such as infection, trauma, or neurodegeneration occur, neutrophils rapidly infiltrate the central nervous system to exert their defensive effects. However, neutrophils may adversely affect the host when they uncontrollably release neutrophil extracellular traps upon persistent neuroinflammation. This review focused on recent advances in understanding the mechanisms and effects of neutrophil extracellular traps release in neurological diseases, and we also discuss the role of molecules that regulate neutrophil extracellular traps release in anticipation of clinical applications in neurological diseases.

Cost-effectiveness analysis of bevacizumab combined with lomustine in the treatment of progressive glioblastoma using a Markov model simulation analysis.

Background: Progressive glioblastoma (GBM) is a malignancy with extremely poor prognosis. Chemotherapy is one of the approved systemic treatment modalities. The aim of this study is to assess the cost-effectiveness of using bevacizumab (BEV) in combination with lomustine (LOM) regimen for the treatment of progressive glioblastoma in China. Methods: The estimation results are derived from a multicenter randomized phase III trial, which demonstrated improved survival in GBM patients receiving BEV+LOM combination therapy. To calculate the incremental cost-effectiveness ratio (ICER) from the perspective of Chinese society, a Markov model was established. Univariate deterministic analysis and probabilistic sensitivity analysis were employed to address the uncertainties within the model. Results: Compared to LOM monotherapy, the total treatment cost for BEV+LOM combination therapy increased from $2,646.70 to $23,650.98. The health-adjusted life years (QALYs) for BEV+LOM combination therapy increased from 0.26 QALYs to 0.51 QALYs, representing an increment of 0.25 QALYs. The incremental cost-effectiveness ratio (ICER) was $84,071.12. The cost-effectiveness curve indicates that within the willingness-to-pay (WTP) range of $35,906 per QALY, BEV+LOM combination therapy is not a cost-effective treatment option for unresectable malignant pleural mesothelioma patients. Conclusions: Taken as a whole, the findings of this study suggest that, from the perspective of payers in China, BEV+LOM combination therapy as a first-line treatment for GBM is not a cost-effective option. However, considering the survival advantages this regimen may offer for this rare disease, it may still be one of the clinical treatment options for this patient population.

Positional cloning and characterization reveal the role of TaSRN-3D and TaBSR1 in the regulation of seminal root number in wheat.

Seminal roots play a critical role in water and nutrient absorption, particularly in the early developmental stages of wheat. However, the genes responsible for controlling SRN in wheat remain largely unknown. Genetic mapping and functional analyses identified a candidate gene (TraesCS3D01G137200, TaSRN-3D) encoding a Ser/Thr kinase glycogen synthase kinase 3 (STKc_GSK3) that regulated SRN in wheat. Additionally, experiments involving hormone treatment, nitrate absorption and protein interaction were conducted to explore the regulatory mechanism of TaSRN-3D. Results showed that the TaSRN-3D4332 allele inhibited seminal roots initiation and development, while loss-of-function mutants showed significantly higher seminal root number (SRN). Exogenous application of epi-brassinolide could increase the SRN in a HS2-allelic background. Furthermore, chlorate sensitivity and 15N uptake assays revealed that a higher number of seminal roots promoted nitrate accumulation. TaBSR1 (BIN2-related SRN Regulator 1, orthologous to OsGRF4/GL2 in rice) acts as an interactor of TaSRN-3D and promotes TaBSR1 degradation to reduce SRN. This study provides valuable insights into understanding the genetic basis and regulatory network of SRN in wheat, highlighting their roles as potential targets for root-based improvement in wheat breeding.

Natural variation of STKc_GSK3 kinase TaSG-D1 contributes to heat stress tolerance in Indian dwarf wheat.

Heat stress threatens global wheat (Triticum aestivum) production, causing dramatic yield losses worldwide. Identifying heat tolerance genes and comprehending molecular mechanisms are essential. Here, we identify a heat tolerance gene, TaSG-D1E286K, in Indian dwarf wheat (Triticum sphaerococcum), which encodes an STKc_GSK3 kinase. TaSG-D1E286K improves heat tolerance compared to TaSG-D1 by enhancing phosphorylation and stability of downstream target TaPIF4 under heat stress condition. Additionally, we reveal evolutionary footprints of TaPIF4 during wheat selective breeding in China, that is, InDels predominantly occur in the TaPIF4 promoter of Chinese modern wheat cultivars and result in decreased expression level of TaPIF4 in response to heat stress. These sequence variations with negative effect on heat tolerance are mainly introduced from European germplasm. Our study provides insight into heat stress response mechanisms and proposes a potential strategy to improve wheat heat tolerance in future.

Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.

OBJECTIVES: This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms. METHODS: Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (n = 2440) and development (n = 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP). RESULTS: A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis. CONCLUSIONS: The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.