Urea cycle disorder presenting as bilateral mesial temporal sclerosis - an unusual cause of seizures: a case report and review of the literature

A Machine Learning Assisted Non-Enzymatic Electrochemical Biosensor to Detect Urea Based on Multi-Walled Carbon Nanotube Functionalized with Copper Oxide Micro-Flowers

Detecting urea is crucial for diagnosing related health conditions and ensuring timely medical intervention. The addition of machine learning (ML) technologies has completely changed the field of biochemical sensing, providing enhanced accuracy and reliability. In the present work, an ML-assisted screen-printed, flexible, electrochemical, non-enzymatic biosensor was proposed to quantify urea concentrations. For the detection of urea, the biosensor was modified with a multi-walled carbon nanotube-zinc oxide (MWCNT-ZnO) nanocomposite functionalized with copper oxide (CuO) micro-flowers (MFs). Further, the CuO-MFs were synthesized using a standard sol-gel approach, and the obtained particles were subjected to various characterization techniques, including X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR) spectroscopy. The sensor's performance for urea detection was evaluated by assessing the dependence of peak currents on analyte concentration using cyclic voltammetry (CV) at different scan rates of 50, 75, and 100 mV/s. The designed non-enzymatic biosensor showed an acceptable linear range of operation of 0.5-8 mM, and the limit of detection (LoD) observed was 78.479 nM, which is well aligned with the urea concentration found in human blood and exhibits a good sensitivity of 117.98 mA mM-1 cm-2. Additionally, different regression-based ML models were applied to determine CV parameters to predict urea concentrations experimentally. ML significantly improves the accuracy and reliability of screen-printed biosensors, enabling accurate predictions of urea levels. Finally, the combination of ML and biosensor design emphasizes not only the high sensitivity and accuracy of the sensor but also its potential for complex non-enzymatic urea detection applications. Future advancements in accurate biochemical sensing technologies are made possible by this strong and dependable methodology.

Unraveling the Link: Seizure Characteristics and Ammonia Levels in Urea Cycle Disorder During Hyperammonemic Crises

BACKGROUND

This retrospective clinical study performed at a single clinical center aimed to identify the prevalence of seizures in individuals with urea cycle disorders (UCDs) with and without hyperammonemic (HA) crises. In addition, we sought to correlate the utility of biochemical markers and electroencephalography (EEG) in detecting subclinical seizures during HA.

METHODS

Medical records of individuals with UCDs enrolled in Urea Cycle Disorders Consortium Longitudinal Study (UCDC-LS) (NCT00237315) at Children's National Hospital between 2006 and 2022 were reviewed for evidence of clinical and subclinical seizuress during HA crises, and initial biochemical levels concurrently.

RESULTS

Eighty-five individuals with UCD were included in the analyses. Fifty-six of the 85 patients (66%) experienced HA crises, with a total of 163 HA events. Seizures are observed in 13% of HA events. Among all HA events with concomitant EEG, subclinical seizures were identified in 27% of crises of encephalopathy without clinical seizures and 53% of crises with clinical seizures. The odds of seizures increases 2.65 (95% confidence interval [CI], 1.51 to 4.66) times for every 100 μmol/L increase in ammonia and 1.14 (95% CI, 1.04 to 1.25) times for every 100 μmol/L increase in glutamine.

CONCLUSIONS

This study highlights the utility of EEG monitoring during crises for patients presenting with clinical seizures or encephalopathy with HA. During HA events, measurement of initial ammonia and glutamine can help determine risk for seizures and guide EEG monitoring decisions.

Non-enzymatic disposable electrochemical sensors based on CuO/Co3O4@MWCNTs nanocomposite modified screen-printed electrode for the direct determination of urea

A new electrochemical impedimetric sensor for direct detection of urea was designed and fabricated using nanostructured screen-printed electrodes (SPEs) modified with CuO/Co3O4 @MWCNTs. A facile and simple hydrothermal method was achieved for the chemical synthesis of the CuO/Co3O4 nanocomposite followed by the integration of MWCNTs to be the final platform of the urea sensor. A full physical and chemical characterization for the prepared nanomaterials were performed including Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), contact angle, scanning electron microscope (SEM) and transmission electron microscopy (TEM). Additionally, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to study the electrochemical properties the modified electrodes with the nanomaterials at different composition ratios of the CuO/Co3O4 or MWCNTs. The impedimetric measurements were optimized to reach a picomolar sensitivity and high selectivity for urea detection. From the calibration curve, the linear concentration range of 10-12-10-2 M was obtained with the regression coefficient (R2) of 0.9961 and lower detection limit of 0.223 pM (S/N = 5). The proposed sensor has been used for urea analysis in real samples. Thus, the newly developed non-enzymatic sensor represents a considerable advancement in the field for urea detection, owing to the simplicity, portability, and low cost-sensor fabrication.

Effects of Dufulin on Oxidative Stress and Metabolomic Profile of Tubifex

Dufulin is a highly effective antiviral pesticide used in plants. In this study, a seven-day experiment was conducted to evaluate the effects of Dufulin at five different concentrations (1 × 10⁻⁴, 1 × 10⁻³, 1 × 10⁻², 0.1, and 1 mg/L) on Tubifex. LC-MS-based metabolome analysis detected a total of 5356 features in positive and 9110 features in negative, of which 41 showed significant changes and were identified as differential metabolites. Four metabolic pathways were selected for further study. Detailed analysis revealed that Dufulin exposure affected the urea cycle of Tubifex, probably via argininosuccinate lyase (ASL) inhibition. It also affected the fatty acid metabolism, leading to changes in the concentration of free fatty acids in Tubifex. Furthermore, the changes in metabolites after exposure to Dufulin at 1 × 10⁻² mg/L were different from those at the other concentrations.