The Association Study Between Cytokines and the Risk for Cerebral PalsyWang, Wang, Yang
et alMediators Inflamm (2025) 2025, 3742331
Abstract: Background: Cerebral palsy (CP) is a debilitating condition characterized by abnormal movement or posture beginning early in development. Recent evidence has shown that immunological abnormalities are associated with an increased risk of CP. However, there are no valuable biomarkers for CP diagnosis. Methods: In this case-control study, we recruited 108 children with CP and 52 healthy children as controls. The white blood cell (WBC) counts and the levels of inflammatory markers (interleukin-1β (IL-1β), sIL-2R, interleukin-6 (IL-6), IL-8, IL-10, and tumor necrosis factor-α (TNF-α)), neuron-specific enolase (NSE), immunoglobulin E (IgE), and C3/C4 in the blood were measured and the results were statistically analyzed. Subgroup analyzes based on age, complications, and clinical subtypes were also carried out. Results: Compared with the controls, CP patients had elevated levels of NSE, sIL-2R, and TNF-α. There were no differences in WBC count, IL-1β, IL-6, IL-8, IL-10, IgE, C3, or C4. Subgroup analysis revealed significant differences in the personal-social developmental quotient (DQ) among the different CP subtypes. We found that TNF-α, sIL-2R, gross motor DQ, and adaptive DQ were greater in children with CP without epilepsy (EP) than in those with EP. Correlation analysis revealed positive correlations between TNF-α and sIL-2R, gross motor DQ, fine motor DQ, adaptive DQ, and personal-social DQ; moreover, sIL-2R was positively correlated with TNF-α, gross motor DQ, adaptive DQ, personal-social DQ, and eosinophil (EO) count and negatively correlated with age. NSE and TNF-α were associated with a 1.64-fold and 1.66-fold increased risk of CP, respectively. The peripheral blood NSE and TNF-α levels exhibited good diagnostic value for CP. Moreover, receiver operating characteristic (ROC) curve analysis revealed a significant increase in the area under the curve (AUC) when these indicators were combined. Conclusions: This study revealed significant associations between NSE and TNF-α and CP risk, suggesting that NSE and TNF-α might be useful blood biomarkers for identifying patients at high risk of CP.Copyright © 2025 Baotian Wang et al. Mediators of Inflammation published by John Wiley & Sons Ltd.
Status epilepticus in the neonateNagarajan, Ghosh
BMJ Paediatr Open (2025) 9 (1)
Abstract: Status epilepticus in the neonate (NSE) is a medical emergency that often results in dire consequences. Minimising injury from NSE is essential. The diagnosis of NSE can be challenging as neonates frequently have electrographic only seizures and an EEG is essential for recognition of seizures and seizure burden. The lack of a universally accepted definition of NSE, possible adverse effects from commonly used antiseizure medications, debate regarding the best treatment packages for NSE, limited access to EEG and investigations for aetiology of NSE add to the clinical conundrum. In this review, we aim to present what is known, highlight the importance of EEG monitoring for diagnosis and treatment, discuss what is not known and suggest a practical paradigm for the management of NSE.© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.
Immunohistochemical and histomorphologic characterization of canine neoplasms of the disseminated neuroendocrine systemChampion, Miller, Parry
et alVet Pathol (2025)
Abstract: Canine neoplasms of the diffuse neuroendocrine system are an enigmatic and heterogeneous group of neoplasms with a wide spectrum of immunohistochemical properties and morphologic features. Through the utilization of tissue microarrays, 82 canine neoplasms of the disseminated neuroendocrine system from 16 different anatomic locations were evaluated. The prototypical canine neoplasm of the disseminated neuroendocrine system was composed of rounded polygonal neoplastic cells arranged in packets supported by delicate fibrovascular stroma. Neoplastic cells typically had moderate quantities of pale eosinophilic cytoplasm stippled by numerous fine argyrophilic granules, round nuclei with finely stippled chromatin, and inconspicuous nucleoli. Immunohistochemical assays utilized in this study included chromogranin A, neuron-specific enolase (NSE), microtubule-associated protein 2 (MAP2), pan-cytokeratin, oligodendrocyte transcription factor 2 (OLIG2), protein gene product 9.5 (PGP9.5), vimentin, synaptophysin, neuronal nuclei (NeuN), S100, SRY-related HMG-box 10 (SOX10), glial fibrillary acidic protein (GFAP), insulinoma-associated protein 1 (INSM1), CD56, and antigen Kiel 67 (Ki67). The 4 immunohistochemical assays that were positive in over 50% of cases included PGP9.5 (77/82, 94%), NSE (68/82, 83%), synaptophysin (59/82, 72%), and chromogranin A (56/82, 69%). In 81/82 (99%) cases, neoplastic cells immunolabeled with at least 1 of these 4 assays, and thus, these 4 immunohistochemical assays are deemed most useful when attempting to substantiate that a neoplasm is of neuroendocrine origin.
Assessment and estimation of runoff and soil loss using novel machine learning techniques for conservation bench terracesKumar, Kumar, Sharma
et alSci Total Environ (2025) 973, 179093
Abstract: Conservation of land and water resources, especially in terms of runoff and soil loss, has the utmost priority in enhancing agricultural production, especially in the foothills of the Himalayas. Many engineering measures have been applied to reduce runoff velocity and soil loss. The present study deals with the effectiveness of Conservation Bench Terraces (CBT) as engineering measures constructed in the outer foothills of the Himalayas (ICAR-IISWC, Dehradun, India) to reduce runoff and soil losses in the context of strom size. Further, the development of runoff and soil loss models using available climatic parameters and machine learning techniques. The parameters used were maximum temperature (Tmax, °C), minimum temperature (Tmin, °C), soil temperature (Tsoil, °C), rainfall (mm), pan evaporation (mm), runoff (mm), and soil loss (Mg/ha) during the year 2007-2015. The machine learning techniques, artificial neural network (ANN), linear function support vector machine (SVM-L), radial function support vector machine (SVM-R), multiple linear regression (MLR) along with hybridization of ANN and both function of SVM with wavelet transform as WANN, WSVM-L and WSVM-R, respectively were employed for the estimation of runoff and soil loss. Their performance evaluation was also assessed with the well accepted quantitative and qualitative indicators. The results revealed that the CBT has reduced runoff and soil losses from the experimental plots. The estimation of runoff and sediment were best predicted by SVM-L model with PCC, RMSE, NSE, MAE, and WI values as 0.82 and 0.56, 18.21 and 0.11, 0.41 and 0.16, 13.45 and 0.069, 0.799 and 0.716, respectively for runoff and sediment modelling. The wavelet hybridized models were inaccurate in prediction in this case. Furthermore, sensitivity analysis were carried out and found rainfall was the most sensitive parameter. The SVM-L model could be applied for the estimation of runoff and soil loss from given parameters, which is helpful in planning and designing of CBTs in larger areas. The results indicate CBT's effectiveness in reducing plot-level runoff and soil losses is comparitively high, specially for storm size lesser than 75 mm. The SVM-L model can act as a powerful tool for policymakers and implementing agencies in planning and designing of CBTs.Copyright © 2025 Elsevier B.V. All rights reserved.
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