Journal: ACS sensors

Article Title: GPCR signaling measurement and drug profiling with an automated live-cell microscopy system

doi: 10.1021/acssensors.2c01341

Figure Lengend Snippet: A schematic of how the FALCOscope coordinates between the microscope and the liquid handler using the μManager application program interface (API), the OpenTrons API from the OT-1 and an imaging queue system. Custom MATLAB scripts segment cells from the fluorescent images and output a table of measured single cell responses over time along with associated metadata that can be plotted in various ways.

Article Snippet: To overcome this limitation, we created a highly customizable, MATLAB-based platform: FALCOscope (Flexible Automated Liquid-handling Combined Microscope) that coordinates an OpenTrons liquid handler and a fluorescent microscope to automate drug treatments, fluorescence imaging and single cell analysis.

Techniques: Microscopy, Imaging

Journal: Annals of Noninvasive Electrocardiology

Article Title: The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

doi: 10.1111/anec.13015

Figure Lengend Snippet: Subject characteristics, inclusion, and exclusion criteria.

Article Snippet: Immanuel et al. ( ) , N/A , Holter ECG (THEW) Automated signal processing toolbox (MATLAB) , Frequency binned averaged ECGs and TW extraction (lead I) Boltzmann sigmoidal functions (upslope, downslope, switch) 9th order polynomial functions (upslope, downslope) Neural network classifiers Normal QTc subgroup analysis (400–450 ms) , TW parameters: Sigmoidal and polynomial classifiers LQTS subtypes>conventional parameters ( p < .0001) No difference control vs. LQTS ( p = .19) or LQT1 vs. LQT2 Neural network classifiers: Control vs. LQTS: 92% LQT1 vs. LQT2: 88% Normal QTc subgroup analysis: TWM: 90% QTC: 71%.

Techniques: Control, Medications, Mutagenesis, Positive Control, Pregnancy Test (hCG) Assay

Journal: Annals of Noninvasive Electrocardiology

Article Title: The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

doi: 10.1111/anec.13015

Figure Lengend Snippet: Summary of results for acquired long QT syndrome.

Article Snippet: Immanuel et al. ( ) , N/A , Holter ECG (THEW) Automated signal processing toolbox (MATLAB) , Frequency binned averaged ECGs and TW extraction (lead I) Boltzmann sigmoidal functions (upslope, downslope, switch) 9th order polynomial functions (upslope, downslope) Neural network classifiers Normal QTc subgroup analysis (400–450 ms) , TW parameters: Sigmoidal and polynomial classifiers LQTS subtypes>conventional parameters ( p < .0001) No difference control vs. LQTS ( p = .19) or LQT1 vs. LQT2 Neural network classifiers: Control vs. LQTS: 92% LQT1 vs. LQT2: 88% Normal QTc subgroup analysis: TWM: 90% QTC: 71%.

Techniques: Diagnostic Assay, Software, Control, Clinical Proteomics, Biomarker Discovery, Plasmid Preparation, Patch Clamp, Blocking Assay, Concentration Assay, Mutagenesis

Journal: Annals of Noninvasive Electrocardiology

Article Title: The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

doi: 10.1111/anec.13015

Figure Lengend Snippet: Selected T wave morphology analysis techniques. (a) ECG time intervals indicating specific T wave parameters, including Tpeak‐Tend interval (ms), T duration (ms), and T amplitude (mV). (bi) Application of sigmoidal classifiers demonstrated using Boltzmann sigmoidal functions: Upslope (red dotted line), downslope (red bold dotted line), and switch (red dashed line), as adapted from Immanuel et al. . (bii) T wave fitting of the repolarizing integral (RI), derived from three Hill parameters: n (red bold slope), V max (red horizontal arrow), K m (red vertical arrow) as adapted from Kanters et al. . (c) T wave features applied by the novel, proprietary T wave program , including T wave area, T wave right and left (mV/s), COG ( x / y ) of T wave; and COG of first and last 25% of T wave (ms) as adapted from Sugrue et al. . (d) T wave architectural patterns

Article Snippet: Immanuel et al. ( ) , N/A , Holter ECG (THEW) Automated signal processing toolbox (MATLAB) , Frequency binned averaged ECGs and TW extraction (lead I) Boltzmann sigmoidal functions (upslope, downslope, switch) 9th order polynomial functions (upslope, downslope) Neural network classifiers Normal QTc subgroup analysis (400–450 ms) , TW parameters: Sigmoidal and polynomial classifiers LQTS subtypes>conventional parameters ( p < .0001) No difference control vs. LQTS ( p = .19) or LQT1 vs. LQT2 Neural network classifiers: Control vs. LQTS: 92% LQT1 vs. LQT2: 88% Normal QTc subgroup analysis: TWM: 90% QTC: 71%.

Techniques: Derivative Assay