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Changes in EGFR activity following CRISPR/Cas9-editing of the EGF binding domain
Evaluation of heavy metal contamination in coastal aquifer groundwater of Alappuzha district (Kerala, India) using OSPRC framework
Oxidative stress-associated genes TPPP3 and VEGFA in COPD revealed by bulk and single-cell sequencing analysis
Impact of neuroendocrine neoplasm-specific systemic treatments on expression and function of CXCR4 in neuroendocrine tumor cells
Abstract As neuroendocrine neoplasms (NEN) undergo increasing dedifferentiation, CXC chemokine receptor type 4 (CXCR4) becomes upregulated. Higher levels of CXCR4 are associated with invasive growth, metastasis formation, and poor prognosis. CXCR4 is considered a potential diagnostic biomarker and a promising target for precision therapies — e.g., endoradiotherapeutic approaches, monoclonal antibodies, or small-molecule inhibitors. A variety of systemic therapies are used to treat metastatic NEN, which may modulate CXCR4 expression and potentially influence the efficacy of future CXCR4-targeted strategies. In the NEN cell lines BON-1, QGP-1, and MS-18, we applied cisplatin, etoposide, streptozotocin, 5-fluorouracil, temozolomide, and everolimus- all systemic agents used in highly proliferative NEN. Following incubation, CXCR4 expression was quantified by qRT-PCR, Western blot, and immunohistochemistry. The functional receptor activity was determined by measuring uptake of the radioligand [68Ga]Pentixafor. Cisplatin induced a significant reduction in CXCR4 mRNA levels in BON-1 and QGP-1 cells ( p < 0.05) and decreased radioligand uptake in QGP-1 and MS-18. Etoposide, 5-FU, and streptozotocin had no significant impact on CXCR4 expression or uptake activity. Temozolomide and Everolimus markedly diminished both CXCR4 mRNA and protein levels with no significant impact on radioligand uptake. In high-grade NEN cell lines, Cisplatin, Everolimus, and Temozolomide substantially diminish CXCR4 expression, with Cisplatin significantly decreasing CXCR4-targeted radioligand uptake. These findings might have an impact on the optimal therapy sequence and patient selection for future CXCR4-targeted approaches. Further, the decreased CXCR4 expression could represent a new mechanism of action of the established drugs Cisplatin, Temozolomide, and Everolimus.
Morphological diversity of pollen and spores in a human-impacted highland forest–agriculture mosaic in northern Thailand
A genetic algorithm-based ensemble framework for wind speed forecasting
A rabbit model of clinically relevant mucosal injury induced by nasogastric tube intubation
Understanding mental health discourse on Reddit with transformers and explainability
Modifying graphene oxide with magnetic nanoparticles and Mg-Al LDHs and its application as an efficient catalyst in organic reactions
Housing structure shapes dengue transmission dynamics in a rapidly urbanizing Malaysian district
A bio inspired hybrid optimization framework for efficient real time malware detection
SBTM: epileptic seizure prediction from EEG signal using deep learning in blockchain-enabled smart healthcare monitoring with IoT networking
Abstract Epileptic Seizure prediction is highly significant for the identification and reduction of high risks related to serious brain injuries, strokes, and brain tumors. Early and accurate diagnosis is vital for providing intervention measures for enhancing the quality of life of the affected individuals. Numerous techniques have been developed based on Machine vision techniques to predict epileptic seizures. Nonetheless, the acquisition of precise epileptic seizure detection with low false positive rates is challenging. Moreover, the emergence of the Internet of Things (IoT) revolutionized healthcare monitoring with technological improvements, aiming to handle the concerns related to data interoperability, scalability, as well as privacy issues. Hence, this research proposes the Smart Healthcare Monitoring Framework, namely Spizella Optimization-based Bidirectional Short Term Memory Network (SBTM), for determining the seizure states, thereby allowing the provision of remote care. Specifically, the proposed model exploits the Bi-LSTM architecture that captures the temporal dependencies and nonlinear dynamics of EEG signals, making the model highly efficient for predicting the seizure patterns. Besides, the Spizella Optimization is applied for fine-tuning the hyperparameters of the classifier, thereby leading to accurate prediction. Experimental results demonstrate that the proposed SBTM model accomplishes superior results by achieving high accuracy, sensitivity, and specificity equivalent to 97.52%, 97.51% and 98.51% with 90% training, outperforming the state-of-the-art techniques. Moreover, the presented approach significantly improves the remote monitoring, guaranteeing on-time medical care, ensuring data security, and enhancing the overall performance of applications in tech-aided healthcare systems.
Cybersickness-induced EEG responses in curved monitor and head-mounted display
Geochemical cycling of arsenic in magmatic systems across supercontinent cycles
Experimental investigation of energy and exergy characteristics of a novel solar collector with swirling reversed circular flow jet impingement
Comparative entropy analysis of 2D transition metal tetrahydroxyquinones via machine learning approaches
Machine learning prediction of live birth after IVF using the morphological uterus sonographic assessment group features of adenomyosis
Abstract Predicting live birth after the first IVF/ICSI treatment is challenging, as many factors may interact to affect IVF/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machine learning algorithms have been shown valuable for detecting complex dependencies and predicting outcomes in different clinical settings. We aimed to develop a prediction model for live birth after IVF/ICSI treatment, using the Extreme Gradient Boosting (XGBoost) algoritm and incorporating the revised Morphological Uterus Sonographic Assessment (MUSA) group features of adenomyosis. We used a machine learning model based on data from 1037 women undergoing their first IVF/ICSI treatment between January 2019 and October 2022. The importance of each variable on the model was illustrated with the Shapley additive explanations algorithm (SHAP) variable importance. The prediction model was presented with the area under receiver operating characteristics curve (ROC). The proposed XGBoost model had a test AUC of 0.66 and accuracy of 0.59. S-AMH was the best variable for predicting live birth with a mean SHAP of 0.21, followed by a regular junctional zone as the best ultrasonographic variable, mean SHAP 0.13. The predictive ability of MUSA features in relation to live birth was limited. Additional variables should be included in future prediction models.