Inhibition of Urea Transporter (UT)-B Modulates LPS-Induced Inflammatory Responses in BV2 Microglia and N2a Neuroblastoma Cells.

Urea is the major nitrogen-containing product of protein metabolism, and the urea cycle is intrinsically linked to nitric oxide (NO) production via the common substrate L-arginine. Urea accumulates in the brain in neurodegenerative states, including Alzheimer's and Huntington's disease. Urea transporter B (UT-B, SLC14A1) is the primary transport protein for urea in the CNS, identified most abundantly in astrocytes. Moreover, enhanced expression of the Slc14a1 gene has been reported under neurodegenerative conditions. While the role of UT-B in disease pathology remains unclear, UT-B-deficient mice display behavioural impairment coupled with urea accumulation, NO disruption and neuronal loss. Recognising the role of inflammation in neurodegenerative disease pathology, the current short study evaluates the role of UT-B in regulating inflammatory responses. Using the specific inhibitor UTBinh-14, we investigated the impact of UT-B inhibition on LPS-induced changes in BV2 microglia and N2a neuroblastoma cells. We found that UTBinh-14 significantly attenuated LPS-induced production of TNFα and IL-6 from BV2 cells, accompanied by reduced release of NO. While we observed a similar reduction in supernatant concentration of IL-6 from N2a cells, the LPS-stimulated NO release was further augmented by UTBinh-14. These changes were accompanied by a small, but significant downregulation in UT-B expression in both cell types following incubation with LPS, which was not restored by UTBinh-14. Taken together, the current evidence implicates UT-B in regulation of inflammatory responses in microglia and neuronal-like cells. Moreover, our findings offer support for the further investigation of UT-B as a novel therapeutic target for neuroinflammatory conditions.

Effects of acquisition device, sampling rate, and record length on kinocardiography during position-induced haemodynamic changes.

BACKGROUND: Kinocardiography (KCG) is a promising new technique used to monitor cardiac mechanical function remotely. KCG is based on ballistocardiography (BCG) and seismocardiography (SCG), and measures 12 degrees-of-freedom (DOF) of body motion produced by myocardial contraction and blood flow through the cardiac chambers and major vessels. RESULTS: The integral of kinetic energy ([Formula: see text]) obtained from the linear and rotational SCG/BCG signals was computed over each dimension over the cardiac cycle, and used as a marker of cardiac mechanical function. We tested the hypotheses that KCG metrics can be acquired using different sensors, and at 50 Hz. We also tested the effect of record length on the ensemble average on which the metrics were computed. Twelve healthy males were tested in the supine, head-down tilt, and head-up tilt positions to expand the haemodynamic states on which the validation was performed. CONCLUSIONS: KCG metrics computed on 50 Hz and 1 kHz SCG/BCG signals were very similar. Most of the metrics were highly similar when computed on different sensors, and with less than 5% of error when computed on record length longer than 60 s. These results suggest that KCG may be a robust and non-invasive method to monitor cardiac inotropic activity. Trial registration Clinicaltrials.gov, NCT03107351. Registered 11 April 2017, https://clinicaltrials.gov/ct2/show/NCT03107351?term=NCT03107351&draw=2&rank=1 .

Regional investigation of UT-B urea transporters in the rat brain.

Recent studies have reported increased levels of urea in the aging brain and various neurological disorders. Additionally, these diseased tissues also have increased expression of the UT-B transporter that regulates urea transport in the brain. However, little is known regarding the actual UT-B protein distribution across the brain in either normal or diseased states. This current study investigated UT-B protein abundance across three regions of the rat brain - anterior, posterior and cerebellum. Endpoint RT-PCR experiments showed that there were no regional differences in UT-B RNA expression (NS, N = 3, ANOVA), whilst Western blotting confirmed no difference in the abundance of a 35 kDa UT-B protein (NS, N = 3-4, ANOVA). In contrast, there was a significant variation in a non-UT-B 100 kDa protein (P < 0.001, N = 3-4, ANOVA), which was also detected by anti-UT-B antibodies. Using the C6 rat astrocyte cell line, Western blot analysis showed that 48-h incubation in either 5 mM or 10 mM significantly increased a 30-45 kDa UT-B protein signal (P < 0.05, N = 3, ANOVA). Furthermore, investigation of compartmentalized C6 protein samples showed the 30-45 kDa signal in the membrane fraction, whilst the 100 kDa non-UT-B signal was predominantly in the cytosolic fraction. Finally, immunolocalization studies gave surprisingly weak detection of rat UT-B, except for strong staining of red blood cells in the cerebellum. In conclusion, this study confirmed that RNA expression and protein abundance of UT-B were equal across all regions of the rat brain, suggesting that urea levels were also similar. However, it also highlighted some of the technical challenges of studying urea transporters at the protein level.

HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis.

A stable predictive model is essential for forecasting the chances of cesarean or C-section (CS) delivery, as unnecessary CS delivery can adversely affect neonatal, maternal, and pediatric morbidity and mortality, and can incur significant financial burdens. Limited state-of-the-art machine learning models have been applied in this area in recent years, and the current models are insufficient to correctly predict the probability of CS delivery. To alleviate this drawback, we have proposed a Henry gas solubility optimization (HGSO)-based random forest (RF), with an improved objective function, called HGSORF, for the classification of CS and non-CS classes. Real-world CS datasets can be noisy, such as the Pakistan Demographic and Health Survey (PDHS) dataset used in this study. The HGSO can provide fine-tuned hyperparameters of RF by avoiding local minima points. To compare performance, Gaussian Naive Bayes (GNB), linear discriminant analysis (LDA), K-nearest neighbors (KNN), gradient boosting classifier (GBC), and logistic regression (LR) have been considered in this research. The ADAptive SYNthetic (ADASYN) algorithm has been used to balance the model, and the proposed HGSORF has been compared with other classifiers as well as with other studies. The superior performance was achieved by HGSORF with an accuracy of 98.33% for the PDHS dataset. The hyperparameters of RF have also been optimized by using commonly used hyperparameter-optimization algorithms, and the proposed HGSORF provided comparatively better performance. Additionally, to analyze the causes of CS and their significance, the HGSORF is explained locally and globally using eXplainable artificial intelligence (XAI)-based tools such as SHapely Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). A decision support system has been developed as a potential application to support clinical staffs. All pre-trained models and relevant codes are available on: https://github.com/MIrazul29/HGSORF_CSection.

An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP.

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub.