Yaqi Zhang

The discharge images with different types could express the discharge process at different angles, which plays an important role in discharge observation. ICCD (Intensified Charge-Coupled Device) image is a key tool for observing and interpreting the fast dynamic discharge process because of the nanosecond scale exposure duration. There are still few analyses of the low-level features in ICCD images of meter-level gap discharge under lightning impulse and research on its correlation with current. Therefore, based on the Electron Multiplying ICCD, repetitive discharge experiments of the 1 m rod-plate air gap under standard lightning impulse were carried out. Synchronous discharge intensity images with nanosecond exposure time and time interval, discharge current and voltage were collected. Combined with the analysis of image characteristics and discharge mechanism, the correlation between the longitudinal average light intensity along the rod-plate axis in images and the discharge current was proposed and verified. And the corresponding empirical formula of two parameters above was given and discussed. By analyzing the correlation between image characteristics and current, it is helpful for achieving a quantifiable description of discharge status and discharge characteristics, and providing new research ideas for discharge space charge distribution and current monitoring methods.

Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There’s currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Background Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. The prompt recognition of CHD can facilitate timely and appropriate intervention, reducing the probability of complications and enhancing the prognosis for impacted newborns. However, unlike other rare conditions routinely identified through federal and state newborn screening (NBS) programs, there’s currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. Objective This study was set to identify reliable metabolite biomarkers with clinical relevance, with the aim to assess feasibility of screening and subtype classification of CHD utilizing the DBS newborn screening method. Methods We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories (tetralogy of Fallot, inherited arrhythmia syndrome, neonatal cardiomyopathy). A robust, DBS-oriented metabolomic method, employing both global and targeted strategies based on liquid chromatography with tandem mass spectrometry (LC-MS/MS), was developed. To verify the reliability of this metabolic profiling, we conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records. Additionally, for hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Finally, logistic and LightGBM models were established to aid in CHD discrimination and classification. Results Our metabolomic workflow demonstrated consistent and reliable quantification of metabolites in DBS samples stored at the California Department of Public Health (CDPH) for up to 15 years. Through this process, we discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites as potential biomarkers for CHD assessment, and an additional twelve metabolites as potential markers for classifying different CHD subtypes within DBS samples. Conclusions This study represents the first attempt to validate metabolite profiling results using long-term storage DBS samples procured from the high-quality conditions of the CDPH biobank. The results unveil distinct metabolic discrepancies between various CHD subtypes and healthy controls. Furthermore, our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Background: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

The observation of the air gap discharge process is the basis of revealing the physical mechanism of gas discharge and establishing the accurate numerical simulation model. In order to obtain the variation characteristics at different development stages during the air gap discharge processes under positive and negative lightning impulses in this paper, repetitive discharge tests of 1 m rod-plate air gap under standard lightning impulse were carried out in the National Engineering Laboratory (Kunming) for Ultrahigh-voltage Engineering Technology. An improved discharge observation method, using electron-multiplying intensified charge-coupled device (EMICCD) camera, was adopted in the test. A series of discharge spatial-temporal distribution images with nanosecond exposure time and interval time were captured by changing the shooting time delay of the EMICCD in repetitive discharges. The complete discharge development process was reproduced by the image stitching. The development process and the characteristics of the air gap discharge under lightning impulse were analyzed qualitatively. The characteristics of the measured discharge current under the positive lightning impulse were discussed. The testing results and the analysis show that the shape and the development process of the ionization region of the streamer are quite different under different lightning impulses. There are spherical and fan-shaped streamer regions under the positive and negative lightning impulse, respectively. After the streamer runs through the entire gap, the leader develops from the rod electrode to the plate electrode (under the positive impulse) or from both the rod and the plate electrode to the middle area (under the negative impulse). Then the final jump occurs and the gap is broken down. If the streamer still cannot penetrate the entire gap when the applied impulse voltage exceeds the peak, the gap will not be broken down finally.