Difference Plots Research Articles

Can Automated 3-Dimensional Dixon-Based Methods Be Used in Patients With Liver Iron Overload?

Accurate quantification of liver iron concentration (LIC) can be achieved via magnetic resonance imaging (MRI). Maps of liver T2*/R2* are provided by commercially available, vendor-provided, 3-dimensional (3D) multiecho Dixon sequences and allow automated, inline postprocessing, which removes the need for manual curve fitting associated with conventional 2-dimensional (2D) gradient echo (GRE)-based postprocessing. The main goal of our study was to investigate the relationship among LIC estimates generated by 3D multiecho Dixon sequence to values generated by 2D GRE-based R2* relaxometry as the reference standard. A retrospective review of patients who had undergone MRI scans for estimation of LIC with conventional T2* relaxometry and 3D multiecho Dixon sequences was performed. A 1.5 T scanner was used to acquire the magnetic resonance studies. Acquisition of standard multislice multiecho T2*-based sequences was performed, and R2* values with corresponding LIC were estimated. The comparison between R2* and corresponding LIC estimates obtained by the 2 methods was analyzed via the correlation coefficients and Bland-Altman difference plots. This study included 104 patients (51 male and 53 female patients) with 158 MRI scans. The mean age of the patients at the time of scan was 15.2 (SD, 8.8) years. There was a very strong correlation between the 2 LIC estimation methods for LIC values up to 3.2 mg/g (LIC quantitative multiecho Dixon [qDixon; from region of interest R2*] vs LIC GRE [in-house]: r = 0.83, P < 0.01; LIC qDixon [from segmentation volume R2*] vs LIC GRE [in-house]: r = 0.92, P < 0.01); and very weak correlation between the 2 methods at liver iron levels >7 mg/g. Three-dimensional-based multiecho Dixon technique can accurately measure LIC up to 7 mg/g and has the potential to replace 2D GRE-based relaxometry methods.

MOF‐derived 1D/3D N‐doped porous carbon for spatially confined electrochemical CO2 reduction to adjustable syngas

AbstractElectrochemical reduction of CO2 to syngas (CO and H2) offers an efficient way to mitigate carbon emissions and store intermittent renewable energy in chemicals. Herein, the hierarchical one‐dimensional/three‐dimensional nitrogen‐doped porous carbon (1D/3D NPC) is prepared by carbonizing the composite of Zn‐MOF‐74 crystals in situ grown on a commercial melamine sponge (MS), for electrochemical CO2 reduction reaction (CO2RR). The 1D/3D NPC exhibits a high CO/H2 ratio (5.06) and CO yield (31 mmol g−1 h−1) at −0.55 V, which are 13.7 times and 21.4 times those of 1D porous carbon (derived from Zn‐MOF‐74) and N‐doped carbon (carbonized by MS), respectively. This is attributed to the unique spatial environment of 1D/3D NPC, which increases the adsorption capacity of CO2 and promotes electron transfer from the 3D N‐doped carbon framework to 1D carbon, improving the reaction kinetics of CO2RR. Experimental results and charge density difference plots indicate that the active site of CO2RR is the positively charged carbon atom adjacent to graphitic N on 1D carbon and the active site of HER is the pyridinic N on 1D carbon. The presence of pyridinic N and pyrrolic N reduces the number of electron transfer, decreasing the reaction kinetics and the activity of CO2RR. The CO/H2 ratio is related to the distribution of N species and the specific surface area, which are determined by the degree of spatial confinement effect. The CO/H2 ratios can be regulated by adjusting the carbonization temperature to adjust the degree of spatial confinement effect. Given the low cost of feedstock and easy strategy, 1D/3D NPC catalysts have great potential for industrial application.

Sendaway capillary NT-proBNP in pulmonary hypertension

BackgroundN-terminal pro-B-type natriuretic peptide (NT-proBNP) is a biomarker of cardiac ventricular wall stress that is incorporated into pulmonary hypertension (PH) risk stratification models. Sendaway sampling may enable patients to perform...

Analytical performance of feline plasma on current Heska and IDEXX point-of-care biochemistry analyzers compared with a commercial laboratory analyzer.

Point-of-care (POC) biochemistry analyzers are widely used in small animal clinical practice but infrequently independently assessed for performance. To assess the performance of two current model point-of-care biochemistry analyzers (Heska Element DC and IDEXX Catalyst) compared with a commercial laboratory analyzer (Cobas 8000). One hundred twenty-one cats from a feline hospital population were sampled with plasma results from a single lithium heparin tube assessed on all three analyzers. Plasma biochemistry results from each POC analyzer were compared with the commercial laboratory analyzer using Bland-Altman difference plots and by determining whether the limits of agreement (LOAs) (95% of differences) fell within various quality goals after correcting for inherent bias. Only 7 of 14 analytes on the Heska analyzer and 2 analytes on the IDEXX analyzer attained the most stringent LOA quality goal, which was being within desirable total error based on biologic variation (TEdes). The number of analytes achieving quality goals increased with less stringent standards such as American Society of Veterinary Clinical Pathologists allowable total error (ASVCP TEA) guidelines or if <95% of clinical comparisons reaching these quality goals is considered acceptable. Widespread bias was found between both POC analyzers and the commercial laboratory analyzer. The performance of both POC biochemistry analyzers was variable compared with a commercial laboratory analyzer. Performance goals were only able to be attained after the bias for each analyzer was accounted for by offsetting the LOAs and quality goals set by the mean bias for each analyte on each analyzer.

Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples.

Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples. Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells. The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.

Unravelling COVID-19 waves in Rio de Janeiro city: Qualitative insights from nonlinear dynamic analysis

Since the COVID-19 pandemic was first reported in 2019, it has rapidly spread around the world. Many countries implemented several measures to try to control the virus spreading. The healthcare system and consequently the general quality of life population in the cities have all been significantly impacted by the Coronavirus pandemic. The different waves of contagious were responsible for the increase in the number of cases that, unfortunately, many times lead to death. In this paper, we aim to characterize the dynamics of the six waves of cases and deaths caused by COVID-19 in Rio de Janeiro city using techniques such as the Poincaré plot, approximate entropy, second-order difference plot, and central tendency measures. Our results reveal that by examining the structure and patterns of the time series, using a set of non-linear techniques we can gain a better understanding of the role of multiple waves of COVID-19, also, we can identify underlying dynamics of disease spreading and extract meaningful information about the dynamical behavior of epidemiological time series. Such findings can help to closely approximate the dynamics of virus spread and obtain a correlation between the different stages of the disease, allowing us to identify and categorize the stages due to different virus variants that are reflected in the time series.

Machine Learning-Assisted Computational Screening of Adhesive Molecules Derived from Dihydroxyphenyl Alanine.

Marine mussels adhere to virtually any surface via 3,4-dihydroxyphenyl-L-alanines (L-DOPA), an amino acid largely contained in their foot proteins. The biofriendly, water-repellent, and strong adhesion of L-DOPA are unparalleled by any synthetic adhesive. Inspired by this, we computationally designed diverse derivatives of DOPA and studied their potential as adhesives or coating materials. We used first-principles calculations to investigate the adsorption of the DOPA derivatives on graphite. The presence of an electron-withdrawing group, such as nitrogen dioxide, strengthens the adsorption by increasing the π-π interaction between DOPA and graphite. To quantify the distribution of electron charge and to gain insights into the charge distribution at interfaces, we performed Bader charge analysis and examined charge density difference plots. We developed a quantitative structure-property relationship (QSPR) model using an artificial neural network (ANN) to predict the adsorption energy. Using the three-dimensional and quantum mechanical electrostatic potential of a molecule as a descriptor, the present quantum NN model shows promising performance as a predictive QSPR model.

Effects of onset of phase transition on binary neutron star mergers

ABSTRACT Quantum Chromodynamics predicts phase transition from hadronic matter to quark matter at high density, which is highly probable in astrophysical systems like binary neutron star mergers. To explore the critical density where such phase transition can occur, we performed numerical relativity simulations of binary neutron star mergers with various masses (equal and unequal binaries). We aim to understand the effect of the onset of phase transition on the merger dynamics and gravitational wave spectra. We generated a set of equations of states by agnostically changing the onset of phase transition, having the hadronic matter part and quark matter part fixed. This particular arrangement of the equation of states explores the scenario of mergers where mixed phases of matter are achieved before or during the merger. Under these circumstances, if the matter properties with hadronic and quark degrees differ significantly, it is reflected in the stability of the final merger product for the intermediate mass binary. We performed a case study on mixed species merger, where one of the binary companions is hybrid star. If quark matter appears at low densities, we observe significant change in post-merger gravitational wave analysis in terms of higher peak frequencies and post-merger frequencies in power spectral density. We report indications expressed as spikes in phase difference plots at merger time for mixed mergers. We found that the expression of phase transition in post-merger gravitational wave signals is more significant for unequal mass binary than for equal mass binary having the same total baryonic mass.

Analyzing the scale dependent effect of urban building morphology on land surface temperature using random forest algorithm

With continuous urban densification, revealing impacts of urban structures on thermal environment is necessary for climate adaptive design. In this study, random forest and partial difference plots were employed to depict the relative importance and interdependent effects of complex building morphology to land surface temperature (LST) variability. The six spatial factors of building density (BD), mean building height (MBH), building height difference (BHD), floor area ratio (FAR), building volume density (BVD) and mean compactness factor (MCF) were calculated at grids of 90, 300, 600 and 900 m. The results showed that BD, MCF and MBH exerted stable and significant impacts on LST with the highest prediction accuracy at 600 m neighborhood scale, and FAR and BVD were the least correlated to LST changes. Meanwhile, the influencing factors presented different correlation patterns with LST. Among them, the increase of BD had a positive linear effect on LST. MCF and MBH were nonlinearly correlated with the LST variation, and their threshold values of cooling effect were also identified. In addition to controlling BD, it also suggested that comprehensively arranging more small-volume buildings as well as increasing building height to enlarge shadow coverage were more conducive to ground heat mitigation.

Mechanism of CO and acetone adsorption on ZnO and Pt-doped ZnO surfaces for gas sensing applications: A DFT investigation

Spin-polarized density functional theory (DFT) is employed to explain the role of oxygen vacancy, pre-adsorbed oxygen ion, and Pt dopant in CO and acetone sensing of ZnO (0001) surface. Adsorption configurations and electronic properties of ZnO and Pt-doped ZnO surfaces are reported. The results show that good adsorption energy is not alone enough to judge the sensitivity of gas sensors, and one has to consider all charge transfer modes. Charge density difference plots reveal that there is a charge transfer mode for moving electrons from acetone to pre-adsorbed oxygen ion which means that the the direct adsorption of acetone may have a chance to participate in gas sensing also.

Titanium-doped graphite-type silicon carbide biosensor for detecting and eliminating gaseous pollution: A green chemistry approach by molecular simulation study

Regarding thermal strength, chemical stability and surface reactivity of silicon carbide (SiC), it is possible to allocate it as a suitable gas detector for commercial application. Therefore, this research was focused on the investigation of the chemo-resistivity properties of SiC nanosheet through doping with the transition metal. Thermochemical, electric and magnetic properties of titanium (Ti)-doped graphene-like monolayer silicon carbide (SiC) sheet have been studied by the first-principles methods based on the density functional theory (DFT) for scavenging of gas molecules of CO, CO2, NO, NO2. The results recommend that the adsorption of these gas molecules on Ti-embedded monolayer SiC sheet is more energetically desired than that on the pristine ones. Gas molecules of CO, CO2, NO, NO2 have been adsorbed on the Ti site of doped monolayer SiC through the formation of covalent bonds. The assumption of chemical adsorptions has been approved by the projected density of states (PDOS) and charge density difference plots. Charge density difference calculations also indicate that the electronic densities were mainly accumulated on the adsorbate of CO, CO2, NO, NO2 gas molecules. The results in this investigation can indicate the competence of transition metal doped silicon carbide nanosheet in sensor devices.

B-178 Clinical Routine Ultrasensitive LC-MS/MS Method Development for Analysis of 17β-Estradiol in Human Serum

Abstract Background The increasing clinical demand for the measurement of circulating 17β-Estradiol at very low concentrations consolidates the need of highly sensitive and specific analytical methodologies. These assays are suitable in situations such as antiestrogen therapy monitoring (use of aromatase inhibitor in breast cancer treatment); diagnosis of suspect inherited sex-steroid metabolic disorders; diagnosis of precocious and delayed puberty in female; hormone replacement therapy in postmenopausal women; and estrogen deficiency in men. Time-consuming methods and lack of sensitivity are common in the quantification of 17β-Estradiol in serum by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The strategy of chemical derivatization is common for analysis of estrogens because it enhances specificity and sensitivity, but this approach becomes laborious in the sample preparation process. For the purpose of solving these analytical challenges, the aim of this work was developing a reliable, sensitive and low-cost LC-MS/MS method without derivatization for clinical routine analysis. Methods Isotope-labeled internal standard 17β-Estradiol-D5 were added to calibrators, quality controls (QC) and serum samples before a liquid-liquid extraction with the organic solvent Diethyl Ether. After reconstitution, the extracts were injected into a Biphenyl column 50 × 2.1 mm, 2,6 µm, chromatographic separated by solvent gradient (Ammonium Fluoride in Water – Acetonitrile) and detected in positive electrospray ionization mode. The analytical method was validated using reference material grade standards and spiked QCs in charcoal stripped pool patient serum at 4 levels by the assessment of linearity, limit of quantification (LoQ), precision inter and intra-assay, accuracy, carryover, method comparison, cross-reactivity and stability (freeze-thawing cycles, bench top, postoperative and stock solution). Results The method has been validated with the analytical measurement range of 2–500 pg/mL. It was successfully optimized to achieve low levels of 17β-Estradiol as proposed with limit of quantitation of 2 pg/mL. Within-run and between-run precision (coefficient of variation) study yielded 1.3%–7.2% and accuracy within 95.7%–112.1%. No carryover, matrix effect and significant cross-reactivity was observed. Estradiol was found to be stable in serum samples under 3 freeze/thawing cycles, 4 h at ambient temperature and 72 h after extraction procedure in auto-injector. The stability of stock solutions was tested in −20 °C freezer and remained stable for 1 month. Method comparison between two reference laboratories (LC-MS/MS method) was performed and the results showed acceptable data for regression statistics (slope (a) = 0.9184; intercept (b) = −0.1576), t-test statistics (bias = 8.5%) and difference plot. Conclusion An ultrasensitive and derivatization-free LC-MS/MS method was developed and it may be beneficial for patients with endocrine disorders associated with estradiol levels.

Impact of solar variability on Indian summer monsoon through large scale circulations

Via large-scale circulations, the role of solar variability on the Indian summer monsoon is investigated. Standardized anomaly is used to identify years of solar maxima and minima. Statistical analysis such as moving mean, empirical mode decomposition, and wavelet analysis are used to determine the plausible relationship between solar variability, large scale circulations such as El- Niño 3.4 SST anomaly, Dipole Mode Index (DMI), and Atlantic Multidecadal Oscillation (AMO), and Indian summer monsoon rainfall (ISMR). In addition, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR) reanalysis data collection is used to assess time-averaged composite anomalies of wind, latent heat, geopotential height during maximum/minimum solar activity years.The 31-years moving mean of DMI shows significant negative correlation (−0.35) with sunspot number and with 31-years moving mean ISMR (−0.28). Similarly, 31-years moving mean of AMO exhibits a substantial positive connection with ISMR (0.68) and Niño 3.4 SST shows significant negative correlation (−0.62) with ISMR. Using intrusive mode function analyses it has noted that India receives rainfall for both phase of solar forcing but from wind studies at 850 hPa and 200 hPa, it is found that the phenomena for ISMR is different during solar maximum and minimum conditions. During solar maximum, a strong Low Level Jet is observed, and during solar minimum, a strong Tropical Easterly Jet is observed. Also, the area of influences of ISMR is different for solar maximum and solar minimum. The mean difference plot of seasonal rainfall shows that during solar maximum, north India receives reasonable amount of rainfall however during solar minimum, south India receives comparatively more rainfall than north India. A weakening of the local Hadley cell during solar minima is also observed.

Headspace versus direct injection for the quantitation of methanol, ethyl acetate, and fusel oils in distilled spirits by gas chromatography – Flame ionization detection

Distilled spirits can be very complex in their sensory or organoleptic compounds. Of significant interest is determination of the concentration of methanol, ethyl acetate, and fusel oils, which include n-propanol, isobutanol, n-butanol, active amyl (2-methyl-1-butanol) and isoamyl (3-methyl-1-butanol) alcohols. Here, we describe a validated method for the analysis of these analytes using a headspace (HS) sampling unit coupled with a gas chromatograph fitted with a flame ionization detector (GC/FID) for profiling these analytes in distilled spirits (n = 26) obtained from local retailers. HS results were compared to the direct injection (DI) GC/FID protocol made available by the US Alcohol and Tobacco Tax and Trade Bureau (TTB), method SSD:TM:200 via correlation and Bland-Altman difference plots to demonstrate that HS-GC/FID is a valid alternative to the direct injection protocols described elsewhere.•A method for the analysis of methanol, ethyl acetate, and fusel oils via headspace sampling coupled to a gas chromatograph fitted with a flame ionization detector (HS-GC/FID) is described.•Samples required no pre-treatment beyond diluting 1 mL of distilled spirit in 4 mL water containing table salt, which resulted in a method with minimal inlet or column maintenance, little sample prepration, and a rapid run time with retention times under 7 min.•Validation by comparing to established protocols using direct injection made available by the US Federal Tax and Trade Bureau (TTB).

Sperm motility assessed by deep convolutional neural networks into WHO categories

Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson’s correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson’s r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson’s r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.

Abstract P334: Comparing A Cuffless Device With Traditional Home Blood Pressure Monitoring In A Remote Hypertension Management Program: Primary Results Of The COOL-BP Study

Background: Innovative BP measurement technology with cuffless devices has the potential to enhance HTN control, especially with remote HTN management. Methods: Patients with uncontrolled BP at Mass General Brigham were managed by navigators and pharmacists in a digital remote HTN management program, based on home BP monitoring (HBPM) with digitally-connected arm cuffs. A subset was enrolled into the 6-month COntinual vs OccasionaL (COOL-BP) study, testing a wrist cuffless device (Aktiia SA, Switzerland). Asynchronous daytime weekly BP averages from both modalities were compared, including all readings from the cuffless device, and guideline-directed twice daily readings from HBPM. Of 38 patients who measured their BP with both devices, 25 collected at least one overlapping week of BPs with each device, with a minimum of 12 valid daytime readings over at least 3 days per week. Results: The scatter plots (Figure, top) showed moderate to strong correlation between 72 weekly average daytime BPs by HBPM and the cuffless device (r=0.57 for SBP; r=0.64 for DBP). The difference plots (Figure, bottom) showed that cuffless SBP was higher than HBPM (4.9±11.3 mmHg, p=0.01), while DBP was lower (-4.2±7.2 mmHg; p=0.003). Importantly, cuffless BPs were non-simultaneous with HBPM, included measurements in different body positions, and were much more numerous, which could help explain the differences between BP averages. Conclusion: Daytime weekly average home BPs with continual monitoring using a cuffless wrist device were comparable to those measured by traditional HBPM. These findings encourage exploring the use of continual cuffless monitoring for the remote management of HTN.

Paper Sensor Modified with MoS2 for Detection of Dopamine Using a Machine-Intelligent Web App Interface.

The sensing behavior of a MoS2-functionalized paper sensor towards dopamine was explored through a combinatorial approach of theoretical analysis, subsequent experimental validation, and machine-learning-driven predictive modeling of the measured electrochemical outputs. The suitability of the chosen 2D material for efficient detection of dopamine was confirmed using density functional theory. The physisorption behavior along with electrostatic interaction due to the incorporation of dopamine on MoS2 was unraveled under the purview of theoretically estimated noncovalent interaction and charge density difference plot. The theoretical Löwdin population analysis elucidates the alteration in oxidation potential of dopamine, as observed in electrochemical experiments. The electrochemical responses of the developed sensor with the spiked serum samples showed an average accuracy of more than 96% with a limit of detection of 10 nM. Furthermore, implementation of a machine-intelligent interactive web app interface improved the resolution of the sensing platform significantly with an enhanced accuracy of nearly 99%.

Impact on result interpretation of correct and incorrect selection of veterinary glucometer canine and feline settings.

Veterinary glucometers should be correctly coded for the patient species; however, coding errors occur in clinical settings and the impact of such errors has not been characterized. We compared glucose concentrations in 127 canine and 37 feline samples using both canine and feline settings on a veterinary glucometer (AlphaTrak; Zoetis). All samples were measured first on the canine setting and then measured using the feline setting. Glucose concentration was also measured using a central laboratory biochemical analyzer (Cobas c311; Roche). Three data comparisons for each species were investigated: incorrectly coded glucometer vs. correctly coded glucometer, correctly coded glucometer vs. Cobas c311, and incorrectly coded glucometer vs. Cobas c311. For each comparison, the following analyses were conducted: Spearman rank correlation coefficient, Bland-Altman difference plot analysis, mountain plot analysis, and Deming regression. For clinical context, Clarke error grids were constructed. There was high positive correlation for all comparisons with both species. For all comparisons, mean difference was low (-0.7 to 0.5 mmol/L for canine samples, 1.0-2.0 mmol/L for feline samples). Incorrect glucometer coding resulted in proportional bias for canine samples and positive constant bias for feline samples, and individual differences could be large (-4.44 mmol/L for one dog, 6.16 mmol/L for one cat). Although the glucometer should be used per the manufacturer's recommendation, coding errors are unlikely to have severe adverse clinical consequences for most patients based on error grid analysis.

Ultrasound-derived fat fraction for detection of hepatic steatosis and quantification of liver fat content.

To compare ultrasound (US) and US-derived fat fraction (UDFF) with magnetic resonance proton density fat fraction (MRI-PDFF) for the detection of hepatic steatosis and quantification of liver fat content. Between October and December 2022, 149 patients scheduled for an abdominal MRI agreed to participate in this study and underwent MRI-PDFF, US and UDFF. Inclusion criteria were: (a) no chronic liver disease or jaundice; (b) no MRI motion artifacts; (c) adequate liver examination at US. Exclusion criteria were: (a) alcohol abuse, chronic hepatitis, cirrhosis, or jaundice; (b) MRI artifacts or insufficient US examination. The median of 10 MRI-PDFF and UDFF measurements in the right hepatic lobe was analyzed. UDFF and MRI-PDFF were compared by Bland-Altman difference plot and Pearson's test. Sensitivity, specificity, positive and negative predictive values, accuracy, and area under the receiver-operator curve (AUC-ROC) of US and UDFF were calculated using an MRI-PDFF cut-off value of 5%. p values ≤ 0.05 were statistically significant. 122 patients were included (61 men, mean age 60years, standard deviation 15years). The median MRI-PDFF value was 4.1% (interquartile range 2.9-6); 37.7% patients had a median MRI-PDFF value ≥ 5%. UDFF and MRI-PDFF had high agreement (p = 0.11) and positive correlation (⍴ = 0.81, p < 0.001). UDFF had a higher diagnostic value than US for the detection of steatosis, with AUC-ROCs of 0.75 (95% CI 0.65, 0.84) and 0.53 (95% CI 0.42, 0.64), respectively. UDFF reliably quantifies liver fat content and improves the diagnostic value of US for the detection of hepatic steatosis.

The Use of Empirical Mode Decomposition on Heart Rate Variability Signals to Assess Autonomic Neuropathy Progression in Type 2 Diabetes

In this study, we investigated the use of empirical mode decomposition (EMD)-based features extracted from electrocardiogram (ECG) RR interval signals to differentiate between different levels of cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). This study involved 60 participants divided into three groups: no CAN, subclinical CAN, and established CAN. Six EMD features (area of analytic signal representation—ASRarea; area of the ellipse evaluated from the second-order difference plot—SODParea; central tendency measure of SODP—SODPCTM; power spectral density (PSD) peak amplitude—PSDpkamp; PSD band power—PSDbpow; and PSD mean frequency—PSDmfreq) were extracted from the RR interval signals and compared between groups. The results revealed significant differences between the noCAN and estCAN individuals for all EMD features and their components, except for the PSDmfreq. However, only some EMD components of each feature showed significant differences between individuals with noCAN or estCAN and those with subCAN. This study found a pattern of decreasing ASRarea and SODParea values, an increasing SODPCTM value, and a reduction in PSDbpow and PSDpkamp values as the CAN progressed. These findings suggest that the EMD outcome measures could contribute to characterizing changes associated with CAN manifestation in individuals with T2DM.