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27 pages, 7418 KiB  
Article
Assessment of CCMP in Capturing High Winds with Respect to Individual Satellite Datasets
by Pingping Rong and Hui Su
Remote Sens. 2024, 16(22), 4215; https://doi.org/10.3390/rs16224215 - 12 Nov 2024
Viewed by 381
Abstract
High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations [...] Read more.
High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%). Full article
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Figure 1
<p>Demonstration of local time (LT) coverages for SMAP (<b>a</b>), ASCATB (<b>b</b>), and ASMR2 (<b>c</b>) on 1 September 2023.</p>
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<p>(<b>a</b>–<b>d</b>) Demonstration of local time (LT) coverages for CYGNSS at the indicated UT time plus or minus 0.75 hours (as shown in each title), on 1 September 2023.</p>
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<p>Each pair of global hourly (4 UT hours per day from February to October 2023) pixel-by-pixel (0.25° × 0.25°) ocean wind speed maps are compared between CCMP and AMSR2, SMAP, ASCAT2, or CYGNSS, and then statistical moments of all such pairs are shown in histograms, represented by different colors. (<b>a</b>–<b>c</b>) Histograms of the mean, standard deviation (STD), and standard error of the mean (SEM) of the percent differences. (<b>d</b>) Histograms of spatial correlation coefficients of these hourly maps. Note that in the legend, the median and standard deviation describe the current histogram’s median and spread.</p>
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<p>CCMP is linearly interpolated from the 4 UTs onto 0.5-hourly intervals, and same statistical moments of percent differences between CCMP and SMAP are calculated to compare with the results based on the 4 UTs per day. The maxima of the red histograms are adjusted (8–10 times) to match the blue curves. The y-axis numbers correspond to the blue histogram. The (<b>a</b>–<b>d</b>) resemble those in <a href="#remotesensing-16-04215-f003" class="html-fig">Figure 3</a>.</p>
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<p>(<b>a</b>) A global map of CCMP for a selected day to demonstrate the distribution of high-wind structures. Both Saola and Haikui (within the white rectangle) are notable, and a magnified regional map is shown in (<b>b</b>).</p>
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<p>Same as <a href="#remotesensing-16-04215-f003" class="html-fig">Figure 3</a>, except that the individual cases are 10° Lon × 10° Lat blocks identified as containing high-wind structures (i.e., TCs) in the low-latitude region between 35°S and 35°N. CYGNSS is not included because, based on our criteria, no high-wind features were identified. The (<b>a</b>–<b>d</b>) resemble those in <a href="#remotesensing-16-04215-f003" class="html-fig">Figure 3</a>.</p>
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<p>AMSR2 maps (<b>top</b>) and CCMP maps (<b>bottom</b>) at coincidences for the five selected high spatial correlation cases, based on the results in <a href="#remotesensing-16-04215-f006" class="html-fig">Figure 6</a>.</p>
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<p>Same as <a href="#remotesensing-16-04215-f007" class="html-fig">Figure 7</a>, except for SMAP.</p>
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<p>Same as <a href="#remotesensing-16-04215-f007" class="html-fig">Figure 7</a>, except for ASCATB.</p>
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<p>Same as <a href="#remotesensing-16-04215-f006" class="html-fig">Figure 6</a>, except for the mid-high latitude region south of 35°S or north of 35°N. The (<b>a</b>–<b>d</b>) resemble those in <a href="#remotesensing-16-04215-f003" class="html-fig">Figure 3</a>.</p>
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<p>AMSR2 maps (<b>top</b>) and CCMP maps (<b>bottom</b>) at coincidences for the five selected high spatial correlation cases in the mid-high latitude region, based on the results in <a href="#remotesensing-16-04215-f010" class="html-fig">Figure 10</a>.</p>
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<p>Same as <a href="#remotesensing-16-04215-f011" class="html-fig">Figure 11</a>, except for SMAP.</p>
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<p>Same as <a href="#remotesensing-16-04215-f011" class="html-fig">Figure 11</a>, except for ASCATB.</p>
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<p>Histograms of the statistics for the SAR and CCMP pixel-by-pixel ocean wind speed comparisons over individual tiles. (<b>a</b>,<b>b</b>) The histograms of tile-wise means, STDs, and SEMs of the pixel-by-pixel percent differences. (<b>c</b>) Spatial correlations of CCMP and SAR ocean wind speed over individual SAR tiles. CCMP values are sampled over the SAR tiles, and the SAR data are resampled onto the CCMP’s grid.</p>
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<p>Same as <a href="#remotesensing-16-04215-f006" class="html-fig">Figure 6</a>, except with a block size of 5° × 5°, to compare with <a href="#remotesensing-16-04215-f014" class="html-fig">Figure 14</a>.</p>
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<p>Selected SAR (<b>top</b>) and CCMP (<b>bottom</b>) TC maps at coincidences with spatial correlations greater than 0.9.</p>
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<p>Demonstration of the TC eye center and eye region identification routines. The black crosses are filled into the detected eye-region size, and the red circle marks the eye center position, which is generally the pixel that possesses the lowest ocean wind speed. (<b>a</b>) and (<b>b</b>) here correspond to (d) and (i) in <a href="#remotesensing-16-04215-f016" class="html-fig">Figure 16</a>, except that they are magnified.</p>
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<p>(<b>a</b>–<b>e</b>) SAR and CCMP TC equivalent radii for different ocean wind speed levels (2.0 m/s intervals) for the five pairs of maps shown in <a href="#remotesensing-16-04215-f016" class="html-fig">Figure 16</a>.</p>
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<p>TC structure comparisons between SAR and CCMP, via histograms of differences in TC eye-center locations (<b>a</b>), eye-region sizes (<b>b</b>), equivalent radii (<b>c</b>), and S–N and W–E asymmetries (<b>d</b>), using all coincident pairs throughout February–October 2023.</p>
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<p>The performance levels of the RF model described by the statistical moments of the scatter plots. (<b>a</b>) Statistical moments when the model is applied to the training set (which are the 75% of ocean wind speed values for the selected set of TCs for model training). (<b>b</b>) The same statistics for the remaining 25% of the wind speed values for the same set of TCs. (<b>c</b>) The same statistics, except for the result from applying the model to a blind TC set. The ty1n2 in the title refers to the case when all predictors in Table 2 are used for the RF model training.</p>
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<p>Histograms of the statistics when the RF model is applied to the individual TC tiles in the blind set. In each panel, the comparison between different curves illustrates the improvement in the predicted ocean wind speed maps relative to the CCMP maps, assuming that the SAR maps are considered the true states, in terms of accuracy (<b>a</b>), bias (<b>b</b>), correlation coefficient (<b>c</b>), and STD of the differences (<b>d</b>).</p>
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<p>Three selected ocean wind speed tiles (in rows 1st–3rd) are used to demonstrate the performance of the ty1, ty2, and ty1n2 (3rd–5th columns) relative to SAR maps (1st column) and the CCMP maps (2nd column).</p>
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12 pages, 1586 KiB  
Article
Comparison of Precision, Agreement, and Accuracy of Two Swept-Source Optical Coherence Tomography Biometers
by Mercè Guarro, Meritxell Vázquez, Juan Carlos Díaz, Sergi Ruiz, Maties Gimeno, Lara Rodríguez, Elena López, Laura Sararols and Marc Biarnés
Diagnostics 2024, 14(21), 2422; https://doi.org/10.3390/diagnostics14212422 - 30 Oct 2024
Viewed by 407
Abstract
Background/Objectives: This study’s aim was to compare the precision, agreement, and accuracy in axial length (AL) measurements of Argos® (Alcon Healthcare, US) and Eyestar 900® (Haag-Streit, Switzerland) swept-source optical coherence tomography (SS-OCT) biometers. Methods: We performed a prospective evaluation [...] Read more.
Background/Objectives: This study’s aim was to compare the precision, agreement, and accuracy in axial length (AL) measurements of Argos® (Alcon Healthcare, US) and Eyestar 900® (Haag-Streit, Switzerland) swept-source optical coherence tomography (SS-OCT) biometers. Methods: We performed a prospective evaluation of two diagnostic devices. Three consecutive measurements of AL with the Argos® and the Eyestar® 900 SS-OCT biometers were conducted in random order in eyes undergoing cataract surgery in Barcelona, Spain. The main endpoint was the median difference in AL between devices. Secondary endpoints included agreement on Bland–Altman plots and 95% limits of agreement (LoAs), repeatability as measured within-subject standard deviation (SW), percent of failed AL measurements, percent of eyes within ±0.50 D and ±1.00 D one month after surgery, and median and mean prediction error. Results: We included 107 eyes of 107 patients (60.8% females, mean age of 73.1 years). The median difference in AL (Argos®-Eyestar 900®) was −0.01 mm (interquartile range [IQR], 0.06), p = 0.01. The 95% LoAs were −0.11 to +0.08 mm, with a trend towards less extreme measurements with Argos® for very short and long eyes. The median (IQR) Sw was 0.0058 (0.0058) and 0.0000 (0.0058) for Argos® and Eyestar 900®, respectively. There were no failed AL measurements with either device (0%, 95% CI = 0% to 3.4%). Overall, 96.1% of eyes were within ±0.50 D and 100% were within ±1.00 D. Conclusions: Argos® and Eyestar 900® provided statistically different but clinically negligible differences in AL. However, they are not interchangeable in very long or short eyes, due to the different principles used to determine AL. Full article
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<p>Swept-source optical coherence tomography biometers evaluated in this study. Left image, Argos<sup>®</sup> (Alcon Healthcare, USA). Right image, Eyestar 900<sup>®</sup> (Haag-Streit, Switzerland).</p>
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<p>Example of swept-source optical coherence tomography B-scan biometry image of the anterior segment up to the posterior pole with each device. Top, Argos<sup>®</sup> biometer. Yellow vertical lines represent the boundaries of each intraocular structure, while green lines are the intensity of the ultrasound signal in the A-scan. Bottom, Eyestar 900<sup>®</sup> biometer. The red lines represent, again, the intensity of the ultrasound echo in the A-scan. In each image and from left to right, the different hyperreflective structures represent the anterior and posterior surfaces of the cornea, the anterior and posterior surfaces of the lens, and finally the retina. The hyporeflective space between the cornea and the lens represents the anterior chamber and the corresponding structure between the lens and the retina is the vitreous cavity. The group refractive index assigns a single mean n to the whole eye to derive the axial length; the sum of segments approach assigns <span class="html-italic">n</span> = 1.376 to the cornea, <span class="html-italic">n</span> = 1.336 to the aqueous and the vitreous humor, and <span class="html-italic">n</span> = 1.410 to the lens.</p>
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<p>Comparison of axial length measurements between devices. Top, kernel density plots (with an Epanechnikov kernel and a bandwidth of 0.34) showing the distribution of the mean axial length measurements with Argos<sup>®</sup> (left) and Eyestar 900<sup>®</sup> (right). The distributions were right-skewed, leptokurtic, and virtually indistinguishable. Bottom, scatterplot comparing the values obtained with each device. Again, the values are very similar, but a slight trend towards lower values in short axial lengths and larger values in long axial lengths can be identified by slight deviation from the identity line (in red), which is more easily appreciated in the higher values.</p>
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<p>Bland–Altman plot for the agreement between Argos<sup>®</sup> and Eyestar 900<sup>®</sup> for axial length measurements. The descending trend implies that, for short eyes (usually associated with hyperopia), Argos<sup>®</sup> provides longer axial length measurements than Eyestar 900<sup>®</sup>; on the other hand, for long eyes (usually associated with myopia), Eyestar 900<sup>®</sup> provides longer measurements than Argos<sup>®</sup>. In other words, the measurements of Eyestar 900<sup>®</sup> were more extreme. The trend was almost linear, and the greatest agreement was centered at approximately 23–23.5 mm. LoA: limit of agreement.</p>
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11 pages, 791 KiB  
Article
The Relationship Between Feelings of Emptiness and Self-Harm Among Thai Patients Exhibiting Borderline Personality Disorder Symptoms: The Mediating Role of the Inner Strengths
by Piangdao Sripunya, Tinakon Wongpakaran and Nahathai Wongpakaran
Medicina 2024, 60(11), 1776; https://doi.org/10.3390/medicina60111776 - 30 Oct 2024
Viewed by 834
Abstract
Background and Objectives: Fifty percent of individuals with borderline personality disorder (BPD) experience self-harm. One of the crucial factors related to self-harm is feelings of emptiness. While inner strengths, such as the Five Precepts, meditation, and equanimity, have been identified as potential [...] Read more.
Background and Objectives: Fifty percent of individuals with borderline personality disorder (BPD) experience self-harm. One of the crucial factors related to self-harm is feelings of emptiness. While inner strengths, such as the Five Precepts, meditation, and equanimity, have been identified as potential buffers against negative mental health outcomes in BPD, their role in mediating the relationship between feelings of emptiness and self-harm is not well-documented. This study aimed to explore how these inner strengths mediate the relationship between feelings of emptiness and self-harm in individuals exhibiting BPD symptoms. Materials and Methods: A total of 302 Thai participants exhibiting BPD symptoms completed several assessments: the SCID-II Personality Disorder Questionnaire for BPD to assess feelings of emptiness and self-harm, the Inner-Strength-Based Inventory (i-SBI) to evaluate the Five Precepts, meditation, and equanimity, and the Outcome Inventory Depression (OI-Depression) to assess depression. Mean and standard deviation were used for continuous variables, such as age and OI-Depression. A t-test assessed mean differences in continuous variables between the self-harm group and the non-self-harm group. Chi-square tests examined differences in categorical variables with three or more levels, such as education. Pearson’s correlation and linear regression analyzed relationships between continuous variables, including i-SBI and OI-Depression scores. Mediation analysis was performed using IBM SPSS and AMOS, with self-harm as the outcome variable, feelings of emptiness as the predictor, and inner strengths as mediators. Results: The participants had a mean age of 36.56, with 65.4% being female. The analysis showed that the Five Precepts, meditation, and equanimity significantly mediated the relationship between feelings of emptiness and self-harm, with a standardized coefficient of β = 0.534 (95% CI = 0.417 to 0.647, p < 0.001). The indirect effect of feelings of emptiness through these inner strengths was significant (β = 0.034, 95% CI = 0.009 to 0.075, p = 0.005). The mediation model explained 38% of the variance in self-harm with a 3% increase, albeit small but significant. Conclusions: This study highlights that inner strengths negatively mediate the relationship between feelings of emptiness and self-harm, indicating that as these inner strengths increase, the direct impact of feelings of emptiness on self-harm decreases. These findings suggest that targeting inner strengths as protective factors could be a valuable strategy in developing interventions aimed at reducing self-harm by addressing the underlying emotional challenges associated with BPD. Full article
(This article belongs to the Section Psychiatry)
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<p>The distribution of sex in each group.</p>
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<p>Mediation model of feelings of emptiness in patients with borderline personality disorder symptoms (BPD symptoms), with the Five Precepts, meditation, equanimity, and self-harm, adjusted for age and OI-Depression. X = Predictor; M1, M2, M3 = Mediator; Y = Outcome; a1, a2, a3, b1, b2, b3, c, c′ = Path coefficients; c = Total direct effect of feelings of emptiness on self-harm; c′ = Direct effect of feelings of emptiness on self-harm mediated by the Five Precepts, meditation, and equanimity. The value for self-harm (Y) is the R-square. Self-harm = non-suicidal self-injury. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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13 pages, 5379 KiB  
Article
Clinical Outcome Patterns of Use of Radium-223 in Patients with Metastatic Castration-Resistant Prostate Cancer
by Colleen Mackenzie, Jasna Deluce, Morgan Black, Emma Churchman, Eric Winquist, Scott Ernst, David T. Laidley, Matthew Parezanovic, Kylea Potvin and Ricardo Fernandes
Curr. Oncol. 2024, 31(11), 6475-6487; https://doi.org/10.3390/curroncol31110480 - 22 Oct 2024
Viewed by 518
Abstract
Introduction: Radium-223 dichloride (radium-223) is a bone-targeting radioisotope therapy that aids in the survival of patients with metastatic castration-resistant prostate cancer (mCRPC) to bones. This study aimed to describe the clinical characteristics and outcomes of patients with mCRPC treated with radium-223 in a [...] Read more.
Introduction: Radium-223 dichloride (radium-223) is a bone-targeting radioisotope therapy that aids in the survival of patients with metastatic castration-resistant prostate cancer (mCRPC) to bones. This study aimed to describe the clinical characteristics and outcomes of patients with mCRPC treated with radium-223 in a real-world setting. Methods: This was a retrospective study of patients with mCRPC treated with radium-223 between 2016 and 2020 at the London Health Sciences Centre in London, Canada. The baseline characteristics between the patients receiving 1–3 and 4–6 treatment cycles were compared using a two-sample t-test and Chi-square test. ANOVA was used to determine if there was a difference in each diagnostic variable per treatment cycle. Kaplan–Meier curves were generated to estimate progression-free survival (PFS) and overall survival in the patients treated with different numbers of cycles. Results: Fifty eligible patients were identified. The median age was 71 years (IQR: 66–76). Most patients (62%) received radium-223 beyond the third-line treatment. The mean number of radium-223 treatments was four. While 60% of the patients received 4–6 injections, 40% received 1–3 injections. Fifty-eight percent (58%) of the patients demonstrated a clinical benefit, with the remainder expressing either disease progression (28%) or stable disease (10%). The patients treated with 4–6 cycles had a delay to disease progression compared to those given 1–3 cycles of radium-223 (F5,35 = 10.52, p < 0.001). A higher alkaline phosphatase level prior to treatment was associated with a longer PFS (z33 = 2.362, p = 0.018). Treatment-related hospitalization for skeletal-related events was noted in 8% of the patients, and 14% required treatment discontinuation due to hematologic toxicity. Conclusions: This study confirms the safety of radium-223 in patients with mCRPC in a real-world setting. The radium-223 treatment was associated with a clinical benefit in the majority of the patients, particularly in those with higher pre-treatment serum alkaline phosphatase levels. Further studies to identify the predictive biomarkers are warranted to better guide the contemporary use of radium-223. Full article
(This article belongs to the Section Genitourinary Oncology)
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<p>Correlations between patient variables before and after first radium-223 treatment cycles, where significant correlations are indicated by trendline (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>ANOVA analysis to determine statistical significance in time to progression between patients treated with different numbers of cycles of radium-223. Boxplot includes the distribution of time to progression per number of treatment cycles. Outliers are indicated with a dot, but they were not removed per intent to treat. Different letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Kaplan–Meier estimates of progression-free survival of patients who underwent 1–3 cycles or 4–6 cycles.</p>
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<p>Kaplan–Meier estimates of progression-free survival of patients who underwent 1–4 cycles or 5–6 cycles.</p>
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<p>Kaplan–Meier estimates of overall survival of patients receiving 1–3 cycles or 4–6 cycles.</p>
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<p>Kaplan–Meier estimates of overall survival of patients receiving 1–4 cycles or 5–6 cycles.</p>
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10 pages, 1515 KiB  
Article
Muscle Oxygen Saturation Dynamics During Upper-Body Resistance Exercise
by Adam M. Gonzalez, Gerald T. Mangine, Anthony G. Pinzone, Kyle S. Beyer and Jeremy R. Townsend
Sensors 2024, 24(20), 6668; https://doi.org/10.3390/s24206668 - 16 Oct 2024
Viewed by 770
Abstract
Research examining the changes in muscle oxygen saturation across multiple sets of resistance exercise is limited. The purpose of this study was to describe the physiological response of muscle oxygenation parameters during upper-body resistance exercise and examine the differential effects of relevant participant [...] Read more.
Research examining the changes in muscle oxygen saturation across multiple sets of resistance exercise is limited. The purpose of this study was to describe the physiological response of muscle oxygenation parameters during upper-body resistance exercise and examine the differential effects of relevant participant characteristics on resistance training performance and muscle oxygen saturation dynamics. Sixty-one recreationally trained men (n = 44; 21.8 ± 2.6 years) and women (n = 17; 20.2 ± 1.8 years) completed five-repetition maximum sets of barbell bench presses at a load equal to 75% 1-RM with a 2 min rest interval. Muscle oxygen saturation (SmO2) dynamics within the anterior deltoid were monitored using a portable near-infrared spectroscopy sensor. The percent change in SmO2 (∆%SmO2), the muscle oxygen re-saturation rate (SmO2RecSlope), and the highest measured SmO2 value during recovery periods (SmO2Peak) were measured. Two-way (sex [men, women] x time [sets 1–5]) repeated measures analyses of variance (ANOVA) were performed on muscle saturation variables. To examine the effect of relevant controlling variables, separate analyses of covariance (ANCOVA) with repeated measures were also performed. No differences were seen with ∆%SmO2 across sets. The main effects for sets occurred for SmO2RecSlope, whereby a decline was noted on sets 4 and 5 (p = 0.001) compared to set 1. Additionally, SmO2Peak was the lowest on set 5 (p < 0.001) compared to all other sets. Moreover, body mass (p = 0.013), diastolic blood pressure (p = 0.044), and mean arterial pressure (p = 0.033) for ∆%SmO2 were the only significant covariates noted amongst the muscle oxygenation variables. In conclusion, no sex differences and only a few set differences in muscle oxygen saturation dynamics were seen without employing any covariates. Body mass, diastolic blood pressure, and mean arterial pressure were identified as factors that could influence observed responses. Full article
(This article belongs to the Section Wearables)
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<p>Moxy NIRS sensor attachment.</p>
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<p>Influence of different covariates on (<b>A</b>) repetitions and (<b>B</b>) volume load completed across five sets of bench press in men and women (mean estimates ± SE). Solid line = men; Dashed line = women; * = Significant (<span class="html-italic">p</span> &lt; 0.05) difference between men and women; † = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set for men; ‡ = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set for women; # = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set in all participants.</p>
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<p>Influence of different covariates on (<b>A</b>) repetitions and (<b>B</b>) volume load completed across five sets of bench press in men and women (mean estimates ± SE). Solid line = men; Dashed line = women; * = Significant (<span class="html-italic">p</span> &lt; 0.05) difference between men and women; † = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set for men; ‡ = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set for women; # = Significant (<span class="html-italic">p</span> &lt; 0.05) difference from previous set in all participants.</p>
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<p>Influence of different covariates on the percent change in muscle oxygen across five sets of bench press in men and women (mean estimates ± SE). Solid line = men; Dashed line = women; ∆%SmO<sub>2</sub> = loss of muscle oxygenation; * = Significant (<span class="html-italic">p</span> &lt; 0.05) difference between men and women.</p>
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14 pages, 422 KiB  
Article
Continuous Glucose Monitor Metrics That Predict Neonatal Adiposity in Early and Later Pregnancy Are Higher in Obesity Despite Macronutrient-Controlled Eucaloric Diets
by Teri L. Hernandez, Sarah S. Farabi, Rachael E. Van Pelt, Nicole Hirsch, Emily Z. Dunn, Elizabeth A. Haugen, Melanie S. Reece, Jacob E. Friedman and Linda A. Barbour
Nutrients 2024, 16(20), 3489; https://doi.org/10.3390/nu16203489 - 15 Oct 2024
Viewed by 659
Abstract
Background: Fasting glucose is higher in pregnancies with obesity (OB); less is known about postprandial (PP) and nocturnal patterns when the diet is eucaloric and fixed or about the continuous-glucose-monitor (CGM) metrics that predict neonatal adiposity (NB%fat). We hypothesized that continuous glucose monitors [...] Read more.
Background: Fasting glucose is higher in pregnancies with obesity (OB); less is known about postprandial (PP) and nocturnal patterns when the diet is eucaloric and fixed or about the continuous-glucose-monitor (CGM) metrics that predict neonatal adiposity (NB%fat). We hypothesized that continuous glucose monitors (CGMs) would reveal higher glycemia in OB vs. normal weight (NW) during Early (14–16 weeks) and Later (26–28 weeks) gestation despite macronutrient-controlled eucaloric diets and elucidate unique predictors of NB%fat. Methods: In a prospective, parallel-group comparative study, a eucaloric diet (NW: 25 kcal/kg; OB: 30 kcal/kg) was provided (50% carbohydrate [20% simple/30% complex; of total calories], 35% fat, 15% protein) to Early and Later gestation groups wearing a blinded CGM for three days. CGM metrics (mean fasting; 1 h and 2 h PP; daytime and nocturnal glucose; percent time-in-range (%TIR: 63–140 mg/dL); PP excursions; and area-under-the-curve [AUC]) were interrogated between groups and as predictors of NB%fat by dual X-ray absorptiometry(DXA). Results: Fifty-four women with NW (BMI: 23 kg/m2; n = 27) and OB (BMI: 32; n = 27) provided their informed consent to participate. Early, the daytime glucose was higher in OB vs. NW (mean ± SEM) (91 ± 2 vs. 85 ± 2 mg/dL, p = 0.017), driven by 2 h PP glucose (95 ± 2 vs. 88 ± 2, p = 0.004). Later, those with OB exhibited higher nocturnal (89 ± 2 vs. 81 ± 2), daytime (95 ± 2 vs. 87 ± 2), 1 h (109 ± 3 vs. 98 ± 2), and 2 h PP (101 ± 3 vs. 92 ± 2) glucose (all p < 0.05) but no difference in %TIR (95–99%). Postprandial peak excursions for all meals were markedly blunted in both the Early (9–19 mg/dL) and Later (15–26 mg/dL). In OB, the Later group’s 24 h AUC was correlated with NB%fat (r = 0.534, p = 0.02). Despite similar weight gain, infants of OB had higher birthweight (3528 ± 107 vs. 3258 ± 74 g, p = 0.037); differences in NB%fat did not reach statistical significance (11.0 vs. 8.9%; p > 0.05). Conclusions: Despite macronutrient-controlled eucaloric diets, pregnancies with OB had higher glycemia Early and Later in gestation; the Later 24 h glucose AUC correlated with NB%fat. However, glycemic patterns were strikingly lower than current management targets. Full article
(This article belongs to the Special Issue Featured Articles on Nutrition and Obesity Management (2nd Edition))
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<p>Patterns of 24 h glycemia measured by CGM in participants with NW and OB, both <span class="html-italic">Early</span> (14–16 weeks, Panel (<b>A</b>)) and <span class="html-italic">Later</span> (26–28 weeks, Panel (<b>B</b>)) in pregnancy. Gray and black dashed lines show mean nocturnal and daytime glucose between the groups.</p>
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13 pages, 704 KiB  
Article
Effectiveness and Side Effect Incidence in a Real-World Digital Weight-Loss Service Using Compounded Semaglutide: A Retrospective Comparative Study
by Louis Talay and Matt Vickers
Obesities 2024, 4(4), 399-411; https://doi.org/10.3390/obesities4040032 - 3 Oct 2024
Viewed by 974
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) were originally developed in the late 1980s as a class of antidiabetic medication. However, research over the past decade has found them to be a safe and effective weight-loss agent, which has led to the approval of [...] Read more.
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) were originally developed in the late 1980s as a class of antidiabetic medication. However, research over the past decade has found them to be a safe and effective weight-loss agent, which has led to the approval of GLP-1 RAs such as Semaglutide as a supplement to lifestyle obesity interventions in multiple countries. When Semaglutide has become commercially unavailable, digital weight-loss services (DWLSs) have prescribed a compounded form of the medication—a practice in which health professionals formulate a replica of the commercial medication to serve ongoing patient needs. Although compounding has been relatively common over the past century, prominent medical bodies have argued that compounding a relatively novel medication such as Semaglutide represents a major safety risk. This study retrospectively compared the weight and side effect outcomes of patients from a large Australian DWLS whose lifestyle coaching was supplemented with either compounded or pure Semaglutide (both groups following the same titration schedule). All data were extracted from the service’s central data repository. To be included in the weight loss analysis, patients needed to have received a minimum of four monthly medication orders between June 2023 and May 2024 and have submitted weight data between 90 and 150 days after the arrival of their first order. All patients who received at least one medication order within the same period were included in the side effect analysis. The mean four-month weight loss percentage was statistically lower in the compounded Semaglutide group (N = 923, M = 9.11, SD = ±5.76) compared to those in the pure Semaglutide group (n = 1858, M = 9.87, SD = ±6.46), t (2032) = −3.15, p = 0.0017. A statistically lower proportion of patients in the compounded Semaglutide group (71.61%) reported at least one side effect than patients in the pure Semaglutide group (77.40%) during the study period, X2 (1, N = 7683) = 32.32, p < 0.001. When side effects were disaggregated into severity categories, a statistical difference was only observed in mild side effects, X2 (1, N = 7683) = 59.16, p < 0.001. A significantly higher rate of patients from the pure Semaglutide group achieved the ten (50.54% vs. 44.64%), X2 (1, N = 7683) = 10.34, p < 0.001, and fifteen (21.42% vs. 12.78%), X2 (1, N = 7683) = 30.43, p < 0.001, percent weight loss thresholds than patients from the compounded Semaglutide group. The findings indicate that compounded Semaglutide can be used as a component of tightly controlled DWLSs with slightly less effectiveness and but with slightly lower side effect incidence when compared to pure Semaglutide. Full article
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<p>Patient flowchart.</p>
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25 pages, 6243 KiB  
Article
Local Weather and Global Climate Data-Driven Long-Term Runoff Forecasting Based on Local–Global–Temporal Attention Mechanisms and Graph Attention Networks
by Binlin Yang, Lu Chen, Bin Yi, Siming Li and Zhiyuan Leng
Remote Sens. 2024, 16(19), 3659; https://doi.org/10.3390/rs16193659 - 30 Sep 2024
Viewed by 798
Abstract
The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate [...] Read more.
The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. Full article
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<p>Schematic description of the experimental procedure.</p>
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<p>Conceptual representation of the process followed to obtain crucial information for the input factors at different time scales. The yellow and green blocks represent precipitation data and global climate indices at series <span class="html-italic">T</span>, respectively. The pink blocks represent a sliding window at the monthly time scale.</p>
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<p>Structure of the graph attention network (GAT)–local–global–temporal attention mechanism–(LGTA)–long–short term memory (LSTM) model. <math display="inline"><semantics> <mrow> <mrow> <mo>{</mo> <mrow> <msubsup> <mi>p</mi> <mi>t</mi> <mn>1</mn> </msubsup> <mo>,</mo> <msubsup> <mi>p</mi> <mi>t</mi> <mn>2</mn> </msubsup> <mo>,</mo> <mo>…</mo> <msubsup> <mi>p</mi> <mi>t</mi> <mi>i</mi> </msubsup> </mrow> <mo>}</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mrow> <mo>{</mo> <mrow> <msubsup> <mi>e</mi> <mi>t</mi> <mn>1</mn> </msubsup> <mo>,</mo> <msubsup> <mi>e</mi> <mi>t</mi> <mn>2</mn> </msubsup> <mo>,</mo> <mo>…</mo> <msubsup> <mi>e</mi> <mi>t</mi> <mi>m</mi> </msubsup> </mrow> <mo>}</mo> </mrow> </mrow> </semantics></math> represent precipitation and the teleconnection factor (global climate indices) inputs at time <span class="html-italic">t</span>, respectively; <span class="html-italic">r<sub>1</sub></span> to <span class="html-italic">r<sub>m</sub></span> represent the historical runoff inputs.</p>
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<p>Structure of the global–temporal attention mechanism (GTA)–long-short term memory (LSTM) model.</p>
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<p>Structure of the global attention mechanism (GA)–long-short term memory (LSTM) model.</p>
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<p>Location of catchments, meteorological stations, and hydrological stations in the study catchment.</p>
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<p>Time series of the monthly runoff for the two case studies (Luning and Pingshan station).</p>
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<p>Variations in MAPE and training time with changes in hyperparameters.</p>
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<p>Simulated versus observed monthly runoff and the fitted regression lines for the graph attention network, (GAT)–local–global–temporal attention mechanism, (LGTA)–long–short term memory (LSTM), and other models.</p>
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<p>Performances of the graph attention network (GAT)–local–global–temporal attention mechanism and the (LGTA)–long–short term memory (LSTM) model and other models in simulating monthly runoff.</p>
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<p>Visualization of the local–global attention weight over twelve months. Brown represents the local attention weight, while the corresponding global attention weight, positioned to the right, is indicated in blue. In the heatmap representing local attention weight, each number denotes an identifier of a global climate index that passed the Hampel test, and the subscript of each number specifies the corresponding month of the global climate index.</p>
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<p>Visualization of the global attention weights of meteorological stations over time of precipitation.</p>
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<p>Visualization of the temporal attention weights of all input factors.</p>
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<p>The elasticity coefficient of global climate indices.</p>
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20 pages, 3215 KiB  
Article
An Oral Botanical Supplement Improves Small Intestinal Bacterial Overgrowth (SIBO) and Facial Redness: Results of an Open-Label Clinical Study
by Mildred Min, Dawnica Nadora, Mincy Chakkalakal, Nasima Afzal, Chaitra Subramanyam, Nimrit Gahoonia, Adrianne Pan, Shivani Thacker, Yvonne Nong, Cindy J. Chambers and Raja K. Sivamani
Nutrients 2024, 16(18), 3149; https://doi.org/10.3390/nu16183149 - 18 Sep 2024
Viewed by 2767
Abstract
Background: Small intestinal bacterial overgrowth (SIBO) is a common, yet underdiagnosed, gut condition caused by gut dysbiosis. A previous study has shown the potential of herbal therapy, providing equivalent results to rifaximin. Objectives: The objective of this study was to assess how the [...] Read more.
Background: Small intestinal bacterial overgrowth (SIBO) is a common, yet underdiagnosed, gut condition caused by gut dysbiosis. A previous study has shown the potential of herbal therapy, providing equivalent results to rifaximin. Objectives: The objective of this study was to assess how the use of an oral botanical regimen may modulate the gut microbiome, facial erythema, and intestinal permeability in those with SIBO. Methods: This was an open-label prospective study of adults that had lactulose breath test-confirmed SIBO. Participants received a 10-week oral supplementation of a Biocidin liquid tincture and GI Detox+. If participants were found to be non-responsive to treatment after 10 weeks with a persistently positive lactulose breath test, a third oral supplement, Olivirex, was administered for an additional 4 weeks. Lactulose breath tests were administered at baseline, weeks 6, 10, and 14 to assess for SIBO status. A high-resolution photographic analysis system was utilized to analyze changes in facial erythema. Stool sample collections and venipuncture were performed to analyze the gut microbiome and intestinal permeability. Results: A total of 33 subjects were screened with breath testing, and 19 subjects were found to have SIBO. Three of the subjects withdrew during the screening period prior to baseline, and sixteen subjects enrolled. Four subjects dropped out after baseline. Hydrogen-dominant SIBO was the most common subtype of SIBO, followed by methane and hydrogen sulfide. The botanical regimen was most effective for hydrogen- and hydrogen sulfide-dominant SIBO, leading to negative breath test results at week 10 in 42.8% and 66.7% of participants, respectively. Compared to baseline, supplementation with the botanical regimen led to positive shifts in short-chain fatty acid-producing bacteria such as A. muciniphila, F. prausnitzii, C. eutectus, and R. faecis by 31.4%, 35.4%, 24.8%, and 48.7% percent at week 10, respectively. The mean abundance of Firmicutes decreased by 20.2%, Bacteroides increased by 30%, and the F/B ratio decreased by 25.4% at week 10 compared to baseline. At week 10, there was a trending 116% increase in plasma LPS/IgG (p = 0.08). There were no significant changes in plasma zonulin, DAO, histamine, DAO/histamine, LPS/IgG, LPS/IgA, or LPS/IgM. Facial erythema was not statistically different at week 6, but at week 10, there was a 20% decrease (p = 0.001) in redness intensity. Among the patients that extended to week 14, there was no statistical change in erythema. Conclusions: Supplementation with an antimicrobial botanical supplemental regimen may have therapeutic potential in hydrogen and hydrogen-sulfide subtypes of SIBO. Furthermore, the botanical supplemental regimen may reduce facial erythema, increase SCFA-producing bacteria, decrease the F/B ratio, and modulate markers of intestinal permeability. Full article
(This article belongs to the Section Prebiotics and Probiotics)
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<p>CONSORT (Consolidated Standards of Reporting Trials) flow diagram.</p>
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<p>Proportion of participants with negative breath test results for H2 (hydrogen), CH4 (methane), or H2S (hydrogen sulfide) SIBO at week 6, 10, and 14 after initially testing positive at baseline.</p>
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<p>Facial erythema intensity among all participants at week 6 and week 10. Image-based photographic analysis of erythema intensity was significantly decreased in (<b>A</b>) all participants, (<b>B</b>) H2-dominant SIBO, (<b>C</b>) CH4-dominant SIBO, (<b>D</b>) H2-dominant SIBO (mixed included), (<b>E</b>) CH4-dominant SIBO (mixed included), (<b>F</b>) H2S-dominant SIBO (mixed included) at week 10. Error bars represent the SEM. * = <span class="html-italic">p</span> &lt; 0.05. ** = <span class="html-italic">p</span> &lt; 0.01. *** = <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>High-resolution facial images taken at (<b>A</b>) baseline and (<b>B</b>) week 10 showing reduced facial erythema.</p>
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<p>Gut microbiome analysis of beneficial bacteria demonstrated shifts in <span class="html-italic">Akkermansia muciniphila</span>, <span class="html-italic">Faecalibacterium prausnitzii</span>, <span class="html-italic">Coprococcus eutectus</span>, <span class="html-italic">and Roseburia faecis</span> at week 10 compared to baseline.</p>
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<p>Gut microbiome analysis of <span class="html-italic">Firmicutes</span> and <span class="html-italic">Bacteroides</span> demonstrated decreased (<b>A</b>) mean abundance of Firmicutes, (<b>B</b>) increased abundance of Bacteroides, and (<b>C</b>) decreased F/B ratio at week 10 compared to baseline. Error bars represent standard deviation (SD).</p>
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<p>Mean plasma intestinal barrier function markers (<b>A</b>) zonulin, (<b>B</b>) DAO, (<b>C</b>) histamine, (<b>D</b>) DAO/histamine, (<b>E</b>) LPS/IgA, (<b>F</b>) LPS/IgG, and (<b>G</b>) LPS/IgM for all participants at baseline and week 10. * = <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Mean plasma intestinal barrier function markers zonulin, DAO, histamine, DAO/histamine, LPS/IgA, LPS/IgG, and LPS/IgM for participants with (<b>A</b>) hydrogen-dominant SIBO, (<b>B</b>) methane-dominant SIBO, and (<b>C</b>) multiple subtypes of SIBO at baseline and week 10. * = <span class="html-italic">p</span> ≤ 0.05.</p>
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16 pages, 282 KiB  
Article
Dual Transformation of Auxiliary Variables by Using Outliers in Stratified Random Sampling
by Mohammed Ahmed Alomair and Umer Daraz
Mathematics 2024, 12(18), 2839; https://doi.org/10.3390/math12182839 - 12 Sep 2024
Viewed by 492
Abstract
To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are [...] Read more.
To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are associated with the study variable, these estimators are particularly useful. Therefore, the precision of the estimators can be effectively improved through the utilization of these rankings. We examine the properties of the proposed class of estimators, including bias and mean squared error (MSE), using a first-order approximation through a stratified random sampling method. To determine the performances and validate the findings mathematically, a simulation study is carried out. Based on the results, the proposed class of estimators performs better in terms of the mean squared error (MSE) and percent relative efficiency (PRE) as compared to other estimators in all scenarios. Furthermore, in order to prove that the performances of the improved class of estimators are better than those of the existing estimators, three data sets are examined in the application section. Full article
(This article belongs to the Special Issue Survey Statistics and Survey Sampling: Challenges and Opportunities)
11 pages, 4780 KiB  
Article
Assessment of Reliability, Agreement, and Accuracy of Masseter Muscle Ultrasound Thickness Measurement Using a New Standardized Protocol
by Mateusz Rogulski, Małgorzata Pałac, Tomasz Wolny and Paweł Linek
Diagnostics 2024, 14(16), 1771; https://doi.org/10.3390/diagnostics14161771 - 14 Aug 2024
Viewed by 697
Abstract
There is no validated method of assessing masseter muscle thickness (MMT) by ultrasound imaging (US). However, this is important to ensure study and measurement quality of MMT by US in future studies, as MMT differs depending on the examined area. Thus, this study’s [...] Read more.
There is no validated method of assessing masseter muscle thickness (MMT) by ultrasound imaging (US). However, this is important to ensure study and measurement quality of MMT by US in future studies, as MMT differs depending on the examined area. Thus, this study’s aim was to present a new standardized method for assessing the MMT by US and to evaluate the reliability, consistency, and accuracy of its measurements. We also compared the results of MMT measurements obtained by US and computer tomography (CT). The study included nine healthy adults. The US and CT scans were collected in a supine rest position with the mandible in relaxed position. US measurements were determined according to a new standardized protocol (with precise probe location). The MMT measured by CT and US over a seven-day interval showed excellent intra-rater reliability. The mean MMT measured by CT was 12.1 mm (1.74) on the right side and 11.9 mm (1.61) on the left side. The mean MMT measured by US was 12.7 mm (2.00) on the right side and 11.5 mm (1.37) on the left side. The mean percent error in MMT measurement between CT and US was below 6%. A strong linear relationship was found between the CT and US measurements of the MMT on both body sides (p < 0.001, r ≥ 0.93). The proposed method of MMT measurement using US demonstrated excellent reliability, yielding results similar to those obtained from CT images. We recommend the use of this standardization protocol in further studies where precise assessment of MMT by US is expected. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Oral and Maxillofacial Disease)
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<p>Determination of probe placement (<b>A</b>). Final probe position and orientation (<b>B</b>).</p>
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<p>Illustration of ultrasound measurements of masseter muscle.</p>
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<p>Presentation of masseter muscle thickness measurements by computer tomography. (<b>a</b>) 3D VR image; (<b>b</b>) horizontal plane; (<b>c</b>) sagittal plane; (<b>d</b>) frontal plane.</p>
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<p>Analysis between computer tomography (CT) and digital caliper (DC) measurements of the mandible–eyelid distance: (<b>A</b>) Bland–Altman plot; (<b>B</b>) Pearson correlation.</p>
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<p>Analysis between computer tomography (CT) and ultrasound (US) measurements of the masseter muscle: (<b>A</b>) Bland–Altman plot; (<b>B</b>) Pearson correlation.</p>
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14 pages, 2844 KiB  
Article
Gut Microbiome Is Related to Cognitive Impairment in Peritoneal Dialysis Patients
by Fabiola Martín-del-Campo, Natali Vega-Magaña, Noé A. Salazar-Félix, Alfonso M. Cueto-Manzano, Marcela Peña-Rodríguez, Laura Cortés-Sanabria, María L. Romo-Flores and Enrique Rojas-Campos
Nutrients 2024, 16(16), 2659; https://doi.org/10.3390/nu16162659 - 12 Aug 2024
Cited by 1 | Viewed by 1951
Abstract
Gut microbiota disturbances may influence cognitive function, increasing uremic toxins and inflammation in dialysis patients; therefore, we aimed to evaluate the association of the gut microbiota profile with cognitive impairment (CI) in patients on automated peritoneal dialysis (APD). In a cross-sectional study, cognitive [...] Read more.
Gut microbiota disturbances may influence cognitive function, increasing uremic toxins and inflammation in dialysis patients; therefore, we aimed to evaluate the association of the gut microbiota profile with cognitive impairment (CI) in patients on automated peritoneal dialysis (APD). In a cross-sectional study, cognitive function was evaluated using the Montreal Cognitive Assessment in 39 APD patients and classified as normal cognitive function and CI. The gut microbiota was analyzed using the 16S rRNA gene sequencing approach. All patients had clinical, biochemical and urea clearance evaluations. Eighty-two percent of patients were men, with a mean age of 47 ± 24 years and 11 (7–48) months on PD therapy; 64% had mild CI. Patients with CI were older (53 ± 16 vs. 38 ± 14, p = 0.006) and had a higher frequency of diabetes mellitus (56% vs. 21%, p = 0.04) and constipation (7% vs. 48%, p = 0.04) and lower creatinine concentrations (11.3 ± 3.7 vs. 14.9 ± 5.4, p = 0.02) compared to normal cognitive function patients. Patients with CI showed a preponderance of S24_7, Rikenellaceae, Odoribacteraceae, Odoribacter and Anaerotruncus, while patients without CI had a greater abundance of Dorea, Ruminococcus, Sutterella and Fusobacteria (LDA score (Log10) > 2.5; p < 0.05). After glucose and age adjustment, Odoribacter was still associated with CI. In conclusion, patients with CI had a different gut microbiota characterized by the higher abundance of indole-producing and mucin-fermenting bacteria compared to normal cognitive function patients. Full article
(This article belongs to the Special Issue Diet, Lifestyle and Cognition)
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<p>Microbiome composition analysis in patients with peritoneal dialysis and cognitive impairment. (<b>A</b>) Alpha diversity measured by observed features, Chao1 and Shannon indices. (<b>B</b>) Beta diversity analysis by Jaccard method. (<b>C</b>) Phylum relative abundance. (<b>D</b>) Genus relative abundance. Statistical analysis was performed with Wilcoxon and ANOSIM.</p>
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<p>Differential abundance analysis and correlations in patients with peritoneal dialysis and cognitive impairment. (<b>A</b>) LEfSe analysis showed that cognitive impairment was characterized by <span class="html-italic">Anaerotruncus</span> and <span class="html-italic">Odoribacter</span>. whereas <span class="html-italic">Ruminococcus</span>, <span class="html-italic">Dorea</span>, <span class="html-italic">Fusobacterium</span> and <span class="html-italic">Sutterella</span> represented patients without cognitive impairment. (<b>B</b>) Pearson correlations showed that the MoCA score had a positive correlation with <span class="html-italic">Fusobacterium</span>, which in turn had a negative correlation with <span class="html-italic">Odoribacter</span>. On the other hand, <span class="html-italic">Odoribacter</span> had a positive correlation with glucose levels, which, at the same time, had a positive correlation with C-reactive protein levels. A positive correlation is represented by red lines and blue lines indicate negative ones. <span class="html-italic">p</span> &lt; 0.05 and r = ±0.5 were considered significant.</p>
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<p>Pearson correlation network linkages of MoCA domains in CI patients. A positive correlation is represented by red lines and blue lines indicate negative ones. <span class="html-italic">p</span> &lt; 0.05 and r = ±0.5 were considered significant.</p>
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<p>Functional profiles of microbial communities and correlation network linkages. The heatmap represents the Spearman correlations between the functional profiles of the microbial communities, MoCA domains, MoCA score, and LPS levels.</p>
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<p>Random forest analysis. Variable importance plot of random forest analysis excluding the glucose level variable. The variables are shown in descending order of importance (according to the mean decrease accuracy value shown); a higher value for the mean decrease in accuracy reflects the higher importance of the variable in the model. NCI: no cognitive impairment; CI-E: cognitive impairment in elder patients; CI-Y: cognitive impairment in younger patients.</p>
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25 pages, 17857 KiB  
Article
Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils
by Ali Sakhaee, Thomas Scholten, Ruhollah Taghizadeh-Mehrjardi, Mareike Ließ and Axel Don
Agriculture 2024, 14(8), 1298; https://doi.org/10.3390/agriculture14081298 - 6 Aug 2024
Viewed by 898
Abstract
Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter [...] Read more.
Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany. Full article
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<p>(<b>a</b>) Box–violin plot of measured C/N ratio, mineral−associated organic matter (MAOM), and particulate organic matter (POM) from the German Agricultural Soil Inventory. (<b>b</b>) Scatterplot of measured versus predicted values for C/N, MAOM, and POM ensemble models.</p>
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<p>(<b>a</b>) Spatial distribution of C/N ratio, (<b>c</b>) mineral−associated organic matter (MAOM), and (<b>e</b>) particulate organic matter (POM) in the top 10 cm of mineral soils in the German Agricultural Soil Inventory, and the spatial distribution of the absolute percent error of (<b>b</b>) C/N ratio, (<b>d</b>) MAOM, and (<b>f</b>) POM. The absolute percent errors of MAOM and POM were calculated from the corrected predictions.</p>
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<p>(<b>a</b>) Spatial distribution of C/N ratio, (<b>c</b>) mineral−associated organic matter (MAOM), and (<b>e</b>) particulate organic matter (POM) in the top 10 cm of mineral soils in the German Agricultural Soil Inventory, and the spatial distribution of the absolute percent error of (<b>b</b>) C/N ratio, (<b>d</b>) MAOM, and (<b>f</b>) POM. The absolute percent errors of MAOM and POM were calculated from the corrected predictions.</p>
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<p>Grouped permutation feature importance (GPFI) (<b>a</b>) and grouped−only permutation feature importance (GOPFI) (<b>b</b>) of the ensemble model for C/N ratio, mineral−associated organic matter (MAOM), and particulate organic matter (POM). The importance is calculated based on the percent relative change in RMSE regarding the permutation method.</p>
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<p>(<b>a</b>) First−order accumulated local effect (ALE) of predictors for soil and parent materials factor for the C/N ratio, (<b>b</b>) mineral−associated organic matter (MAOM), and (<b>c</b>) particulate organic matter (POM). Six classes of each predictor with the three highest and three lowest ALE values are plotted for better visualization. The classes are described in <a href="#app1-agriculture-14-01298" class="html-app">Table S4</a>.</p>
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<p>(<b>a</b>) Spatial prediction of the C/N ratio, mineral−associated organic matter (MAOM) (%), and particulate organic matter (POM) (%) for the top 10 cm of German mineral agricultural soil, with (<b>b</b>) the corresponding model uncertainty.</p>
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<p>SHAP values for the mineral−associated organic matter (MAOM) (<b>a</b>) and particulate organic matter (POM) (<b>b</b>) models. Each dot indicates a sample from the training set. The color of the dot indicates the value of the predictor for the given sample. The darker the dot color, the higher the value of the predictor. The predictors are ordered from highest to lowest based on their absolute mean of the contribution to the prediction. The absolute mean is indicated on the <span class="html-italic">y</span>-axis. The numbers on the <span class="html-italic">x</span>-axis indicate the magnitude that each predictor for the given sample contributed to the final prediction of the model.</p>
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<p>(<b>a</b>) Contribution of predictors in the mineral−associated organic matter (MAOM) model for samples with the lowest absolute percent error, (<b>b</b>) the same samples in the particulate organic matter (POM) model, (<b>c</b>) the POM model for the samples with the lowest absolute percent error, and (<b>d</b>) the same samples in the MAOM model. <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>[</mo> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> <mo>]</mo> </mrow> </semantics></math> is the mean predicted value of all samples. <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> is the mean of the predicted values for the given samples. The numbers on the y-axis indicate the mean contribution of a given predictor to the final prediction of the given samples.</p>
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<p>(<b>a</b>) Contribution of predictors in the mineral−associated organic matter (MAOM) model for samples with the lowest absolute percent error, (<b>b</b>) the same samples in the particulate organic matter (POM) model, (<b>c</b>) the POM model for the samples with the lowest absolute percent error, and (<b>d</b>) the same samples in the MAOM model. <math display="inline"><semantics> <mrow> <mi>E</mi> <mo>[</mo> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> <mo>]</mo> </mrow> </semantics></math> is the mean predicted value of all samples. <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </semantics></math> is the mean of the predicted values for the given samples. The numbers on the y-axis indicate the mean contribution of a given predictor to the final prediction of the given samples.</p>
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<p>(<b>a</b>) The second−order partial dependence plot (PDP) contour plot of particulate organic matter (POM) and (<b>b</b>) mineral−associated organic matter (MAOM) in the modified regressor chain models.</p>
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<p>The first−order accumulated local effect (ALE) plot of the relationship between mineral−associated organic matter (MAOM) and particulate organic matter (POM) (<b>a</b>), MAOM and C/N ratio (<b>b</b>), POM and MAOM (<b>c</b>), and POM and C/N ratio (<b>d</b>) from their corresponding modified RC model. The gray lines represent the interpolated ALE for each CV fold and the red line indicates the overall interpolation. The rug plot indicates the distribution of data from which the first−order ALE was computed.</p>
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14 pages, 8153 KiB  
Article
Customization of Computed Tomography Radio-Opacity in 3D-Printed Contrast-Injectable Tumor Phantoms
by Yuktesh Kalidindi, Aravinda Krishna Ganapathy, Liam Cunningham, Adriene Lovato, Brian Albers, Anup S. Shetty and David H. Ballard
Micromachines 2024, 15(8), 992; https://doi.org/10.3390/mi15080992 - 31 Jul 2024
Viewed by 773
Abstract
Medical Imaging Phantoms (MIPs) calibrate imaging devices, train medical professionals, and can help procedural planning. Traditional MIPs are costly and limited in customization. Additive manufacturing allows for customizable, patient-specific phantoms. This study examines the CT attenuation characteristics of contrast-injectable, chambered 3D-printed phantoms to [...] Read more.
Medical Imaging Phantoms (MIPs) calibrate imaging devices, train medical professionals, and can help procedural planning. Traditional MIPs are costly and limited in customization. Additive manufacturing allows for customizable, patient-specific phantoms. This study examines the CT attenuation characteristics of contrast-injectable, chambered 3D-printed phantoms to optimize tissue-mimicking capabilities. A MIP was constructed from a CT of a complex pelvic tumor near the iliac bifurcation. A 3D reconstruction of these structures composed of three chambers (aorta, inferior vena cava, tumor) with ports for contrast injection was 3D printed. Desired attenuations were 200 HU (arterial I), 150 HU (venous I), 40 HU (tumor I), 150 HU (arterial II), 90 HU (venous II), and 400 HU (tumor II). Solutions of Optiray 350 and water were injected, and the phantom was scanned on CT. Attenuations were measured using ROIs. Mean attenuation for the six phases was as follows: 37.49 HU for tumor I, 200.50 HU for venous I, 227.92 HU for arterial I, 326.20 HU for tumor II, 91.32 HU for venous II, and 132.08 HU for arterial II. Although the percent differences between observed and goal attenuation were high, the observed relative HU differences between phases were similar to goal HU differences. The observed attenuations reflected the relative concentrations of contrast solutions used, exhibiting a strong positive correlation with contrast concentration. The contrast-injectable tumor phantom exhibited a useful physiologic range of attenuation values, enabling the modification of tissue-mimicking 3D-printed phantoms even after the manufacturing process. Full article
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<p>Illustration of the chambered 3D-printed tumor phantom manufacturing process.</p>
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<p>Depiction of the tumor phantom. (<b>A</b>) 3D reconstruction with three separate models. (<b>B</b>) CAD model with Luer Locks indicated by red arrows. (<b>C</b>) STL model in Preform 3.34.2 slicer software with supports. (<b>D</b>) Final 3D-printed tumor phantom on CT gantry. (<b>E</b>) CAD model of tumor phantom with rulers visible in decimeters (dm).</p>
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<p>Depiction of the inside of all tumor phantom hollow chambers after support removal. This version of the tumor phantom provides views of (<b>A</b>) the tumor chamber interior, (<b>B</b>) the arterial chamber interior indicated by red arrows, and (<b>C</b>) the venous chamber interior indicated by the black arrow. (<b>D</b>) Axial view of the chamber-exposed version of the 3D-printed tumor phantom.</p>
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<p>CT images of all six phases of tumor phantom in axial, coronal, and sagittal planes along with ROI placement by Rater 1 in each image depicted by colored circles.</p>
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<p>CT images of tumor phantom arterial phase I in (<b>A</b>) axial, (<b>B</b>) coronal, and (<b>C</b>) sagittal planes with ROI placement (colored circles) and depiction of attenuation measurement.</p>
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<p>(<b>A</b>) Coronal CT image of contrast solution syringe gantry arrangement with concentration expressed as <span class="html-italic">v</span>/<span class="html-italic">v</span> percentage. (<b>B</b>) ROI placement within each syringe. (<b>C</b>) ROI placement for 3.61% and 2.44% contrast solution syringes shown with colored circles and corresponding labels with attenuation measurements.</p>
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<p>Tumor phantom and syringe attenuation (HU) graphed against contrast solution concentration utilized for each phase represented using volume/volume percentage (%).</p>
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<p>ICC interrater analysis of both readers for tumor phantom and syringe attenuations.</p>
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17 pages, 667 KiB  
Article
Parasomnias in Post-Secondary Students: Prevalence, Distress, and Coping Strategies
by Catherine S. Fichten, Eva Libman, Sally Bailes, Mary Jorgensen, Alice Havel, Yuxuan Qin, Laura Creti, Huanan Liao, Bianca Zlotea, Christine Vo, Jillian Budd, Abigaelle Vasseur, Tanya Pierre-Sindor and Georgiana Costin
Behav. Sci. 2024, 14(8), 646; https://doi.org/10.3390/bs14080646 - 26 Jul 2024
Viewed by 1065
Abstract
Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal, or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. [...] Read more.
Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal, or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the current and recent post-secondary student population and to explore their coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia experienced was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced several parasomnias. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies (i.e., coping strategies that help manage distressing feelings) and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these. Therefore, we provide some accepted ways of dealing with these. Full article
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<p>Number of parasomnias experienced per participant.</p>
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<p>Co-Occurrence Relative Frequencies of Parasomnias. Highlighted items are those that occur at least 30% of the time.</p>
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