cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28237534
title: The impact of oleuropein, hydroxytyrosol, and tyrosol on cardiometabolic risk factors: a meta-analysis of randomized controlled trials

description: The so-called Mediterranean diet, with olive oil as a key component, is effective in reducing cardiometabolic disease risk. Olive oil consumption improves blood pressure, insulin levels and resistance, supporting heart health and glycemic control. Its phenolic compounds, including oleuropein (OLE), hydroxytyrosol (HT), and tyrosol (TYR) are hypothesized to likely contribute to these benefits. Thus, this meta-analysis evaluated the clinical effects of dietary supplementation with OLE, HT, and TYR on cardiometabolic outcomes. Fourteen human intervention studies with 594 participants were included. The analysis using a random-effects model showed that OLE, HT, and TYR significantly reduced total cholesterol (SMD = −0.19, CI: −0.37 to −0.01, <i>p</i> = 0.04, <i>I</i><sup>2</sup> = 35%), triacylglycerol (SMD = −0.32, CI: −0.60 to −0.03, <i>p</i> = 0.03, <i>I</i><sup>2</sup> = 73%), and insulin (SMD = −0.42, CI: −0.82 to −0.01, <i>p</i> = 0.04, <i>I</i><sup>2</sup> = 78%). Subgroup analysis showed that, in certain contexts, interventions may be more beneficial for BMI &lt;30, non-Mediterranean, and cardiometabolic disease individuals, while intervention compound, type of intervention, and duration might have differential effects regarding considered outcomes. Overall, the meta-analysis suggests that supplementation with OLE, HT, and TYR may beneficially impact some cardiometabolic parameters, though further studies are needed to confirm these findings.
DOI: 10.6084/m9.figshare.28237534.v1
title: The impact of oleuropein, hydroxytyrosol, and tyrosol on cardiometabolic risk factors: a meta-analysis of randomized controlled trials

description: The so-called Mediterranean diet, with olive oil as a key component, is effective in reducing cardiometabolic disease risk. Olive oil consumption improves blood pressure, insulin levels and resistance, supporting heart health and glycemic control. Its phenolic compounds, including oleuropein (OLE), hydroxytyrosol (HT), and tyrosol (TYR) are hypothesized to likely contribute to these benefits. Thus, this meta-analysis evaluated the clinical effects of dietary supplementation with OLE, HT, and TYR on cardiometabolic outcomes. Fourteen human intervention studies with 594 participants were included. The analysis using a random-effects model showed that OLE, HT, and TYR significantly reduced total cholesterol (SMD = −0.19, CI: −0.37 to −0.01, <i>p</i> = 0.04, <i>I</i><sup>2</sup> = 35%), triacylglycerol (SMD = −0.32, CI: −0.60 to −0.03, <i>p</i> = 0.03, <i>I</i><sup>2</sup> = 73%), and insulin (SMD = −0.42, CI: −0.82 to −0.01, <i>p</i> = 0.04, <i>I</i><sup>2</sup> = 78%). Subgroup analysis showed that, in certain contexts, interventions may be more beneficial for BMI &lt;30, non-Mediterranean, and cardiometabolic disease individuals, while intervention compound, type of intervention, and duration might have differential effects regarding considered outcomes. Overall, the meta-analysis suggests that supplementation with OLE, HT, and TYR may beneficially impact some cardiometabolic parameters, though further studies are needed to confirm these findings.
DOI: 10.5281/zenodo.17495531
title: Maqola
DOI: 10.17632/hzpj48krdt
title: SYNDCAR: Synthetic Vehicle Damage Detection and Parts Segmentation Dataset

description: SYNDCAR (SYNthetic Damage CAR dataset) is a synthetic dataset developed to support research on automated vehicle inspection using computer vision. It integrates two complementary tasks: vehicle damage detection and vehicle parts segmentation.

(i) Damage Classes (4): broken glass, broken lights, cracks, and scratches.

(ii) Vehicle Parts (28): left side window, right side window, left door, right door, right rear quarter panel, left rear quarter panel, right front quarter panel, left rear side window, right rear side window, left front side window, right front side window, roof, left rear wheel, right rear wheel, left front wheel, right front wheel, front windshield, rear windshield, left mirror, right mirror, license plate, rear lights, front lights, front panel, rear panel, left front quarter panel, left rocker panel, and right rocker panel.

In total, SYNDCAR includes 245 high-resolution images, manually annotated and provided in YOLO Detection and Segmentation formats. Vehicle damages are annotated using bounding boxes, while vehicle parts are annotated with polygons.

All images were captured using five different imaging devices, each featuring distinct sensors and resolutions. The filename of every image begins with an identifier (ID) corresponding to the device used for acquisition, ensuring full traceability across the dataset.

The dataset is distributed as a ZIP archive organized into three main folders:

/images/ - Contains all original images;
/labels_damage/ - Includes YOLO-format annotations for vehicle damage;
/labels_parts/ - Contains YOLO-format annotations for vehicle parts.
/data_damage.yaml - Configuration file for YOLO detection with classes names
/data_parts.yaml - Configuration file for YOLO segmentation with classes names
/prepare_dataset.py - Python script to prepare datasets for segmentation and detection tasks in YOLO or COCO format.

This dataset was produced by researchers from the Digital Transformation CoLAB (DTx CoLAB) and the University of Minho, Guimarães, Portugal.
DOI: 10.17632/hzpj48krdt.1
title: SYNDCAR: Synthetic Vehicle Damage Detection and Parts Segmentation Dataset

description: SYNDCAR (SYNthetic Damage CAR dataset) is a synthetic dataset developed to support research on automated vehicle inspection using computer vision. It integrates two complementary tasks: vehicle damage detection and vehicle parts segmentation.

(i) Damage Classes (4): broken glass, broken lights, cracks, and scratches.

(ii) Vehicle Parts (28): left side window, right side window, left door, right door, right rear quarter panel, left rear quarter panel, right front quarter panel, left rear side window, right rear side window, left front side window, right front side window, roof, left rear wheel, right rear wheel, left front wheel, right front wheel, front windshield, rear windshield, left mirror, right mirror, license plate, rear lights, front lights, front panel, rear panel, left front quarter panel, left rocker panel, and right rocker panel.

In total, SYNDCAR includes 245 high-resolution images, manually annotated and provided in YOLO Detection and Segmentation formats. Vehicle damages are annotated using bounding boxes, while vehicle parts are annotated with polygons.

All images were captured using five different imaging devices, each featuring distinct sensors and resolutions. The filename of every image begins with an identifier (ID) corresponding to the device used for acquisition, ensuring full traceability across the dataset.

The dataset is distributed as a ZIP archive organized into three main folders:

/images/ - Contains all original images;
/labels_damage/ - Includes YOLO-format annotations for vehicle damage;
/labels_parts/ - Contains YOLO-format annotations for vehicle parts.
/data_damage.yaml - Configuration file for YOLO detection with classes names
/data_parts.yaml - Configuration file for YOLO segmentation with classes names
/prepare_dataset.py - Python script to prepare datasets for segmentation and detection tasks in YOLO or COCO format.

This dataset was produced by researchers from the Digital Transformation CoLAB (DTx CoLAB) and the University of Minho, Guimarães, Portugal.
DOI: 10.6084/m9.figshare.29464496
title: Optimizing abbreviated injury scale severity using neural networks to enhance the predictive performance of injury severity scores

description: To optimize Abbreviated Injury Scale (AIS) severity using neural networks to improve the predictive performance of the Injury Severity Score (ISS) for patient mortality. Data were obtained from the Japan Trauma Data Bank (2019–2022). Cases involving cardiac arrest upon arrival and pediatric patients younger than 16 years were excluded. A single-layer perceptron neural network was implemented, with AIS 2008 codes as inputs, AIS severities as weights, the logistic function as the activation function, and survival or death as the dependent variable. Data from 2019 to 2021 were used as a derivation set, and data from 2022 were used as a test set. The neural network optimized the weights (AIS severities) using the derivation set, with updating them iteratively through backpropagation. Two sets of AIS severity were created: one in which severity levels were restricted between one and six (restricted AIS severity set) and another without such restrictions (non-restricted AIS severity set). After optimization, the updated severities were determined by rounding the absolute values of the weights to the nearest integer. Using the test data, ISSs were calculated based on the original and the two optimized severity sets, and their predictive performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Data from 107,349 cases (310,232 AIS codes) were analyzed. Among 1,998 AIS 2008 codes, 1,732 (86.7%) were represented in the dataset. ISSs based on the restricted and non-restricted AIS severity sets demonstrated significantly higher predictive performance than the ISS based on the original severity set (AUROC: 0.82 [95% CI, 0.81–0.83] for the restricted AIS severity set, 0.83 [95% CI, 0.82–0.84] for the non-restricted AIS severity set, vs. 0.78 [95% CI, 0.77–0.80] for the original set). Compared to the original AIS severity set, 177 and 231 codes had lower severity levels in the restricted and non-restricted sets, respectively, while 10 and 30 codes had higher severity levels. AIS severities were successfully optimized using a neural network. This approach may support future AIS revisions by improving the accuracy of severity grading and enhancing the predictive performance of the ISS for trauma-related mortality.
DOI: 10.6084/m9.figshare.29464496.v1
title: Optimizing abbreviated injury scale severity using neural networks to enhance the predictive performance of injury severity scores

description: To optimize Abbreviated Injury Scale (AIS) severity using neural networks to improve the predictive performance of the Injury Severity Score (ISS) for patient mortality. Data were obtained from the Japan Trauma Data Bank (2019–2022). Cases involving cardiac arrest upon arrival and pediatric patients younger than 16 years were excluded. A single-layer perceptron neural network was implemented, with AIS 2008 codes as inputs, AIS severities as weights, the logistic function as the activation function, and survival or death as the dependent variable. Data from 2019 to 2021 were used as a derivation set, and data from 2022 were used as a test set. The neural network optimized the weights (AIS severities) using the derivation set, with updating them iteratively through backpropagation. Two sets of AIS severity were created: one in which severity levels were restricted between one and six (restricted AIS severity set) and another without such restrictions (non-restricted AIS severity set). After optimization, the updated severities were determined by rounding the absolute values of the weights to the nearest integer. Using the test data, ISSs were calculated based on the original and the two optimized severity sets, and their predictive performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Data from 107,349 cases (310,232 AIS codes) were analyzed. Among 1,998 AIS 2008 codes, 1,732 (86.7%) were represented in the dataset. ISSs based on the restricted and non-restricted AIS severity sets demonstrated significantly higher predictive performance than the ISS based on the original severity set (AUROC: 0.82 [95% CI, 0.81–0.83] for the restricted AIS severity set, 0.83 [95% CI, 0.82–0.84] for the non-restricted AIS severity set, vs. 0.78 [95% CI, 0.77–0.80] for the original set). Compared to the original AIS severity set, 177 and 231 codes had lower severity levels in the restricted and non-restricted sets, respectively, while 10 and 30 codes had higher severity levels. AIS severities were successfully optimized using a neural network. This approach may support future AIS revisions by improving the accuracy of severity grading and enhancing the predictive performance of the ISS for trauma-related mortality.
DOI: 10.6084/m9.figshare.30062991
title: Relationship with sex-based anatomical differences to lower extremity injury severity in frontal crashes

description: Multiple studies have demonstrated an increased risk of lower extremity injuries for females in frontal crashes. This study aimed to investigate whether sex-based anatomical differences, as measured on computed tomography (CT) scans of the abdomen and pelvis, contribute to lower extremity injury risk. The Crash Injury Research and Engineering Network (CIREN) database (2017–2023) was queried for frontal collisions. Cases were included if the occupant was an adult (≥18 years), seated in the front row, properly restrained, and had an analyzable CT scan of the abdomen and pelvis. Anatomical measurements included soft tissue over the anterior superior iliac spine (ASIS) along three vectors, thigh diameter, thigh anterior soft tissue, subcutaneous adipose tissue (SAT) area, psoas area, lumbar spine bone mineral density (BMD), femur cortical thickness, height, weight, and body mass index (BMI). Ordinal regression analysis was employed to examine the individual associations between these measurements and the highest Abbreviated Injury Scale (AIS) injury severity of the lower extremities, after adjusting for age, change in velocity (delta-V), knee bolster intrusion, toe pan intrusion, seat track position, airbag deployment, and sex. The same analysis was repeated for the highest AIS injury severity in the hip-thigh-knee and the lower leg regions. A total of 195 occupants were included, of which 114 (58%) were female. Among the anatomical measurements from the computed tomography (CT) scans, significant differences between the sexes were observed only in the SAT and psoas areas, with the SAT area being higher in females and the psoas area being higher in males. Ordinal regression analysis revealed that SAT area, weight, and BMI were significantly associated with the severity of lower extremity injuries. These three variables were also significantly associated with hip-thigh-knee injuries but not with injuries to the lower leg. Further analysis indicated that SAT area is a significant mediator of the effect of sex on lower extremity injury severity. The area of subcutaneous tissue at the level of the ASIS was significantly associated with increased severity of lower extremity injuries, especially in the hip-thigh-knee region. This measurement was higher in females and may account for some of the observed differences in injury risk between sexes.
DOI: 10.6084/m9.figshare.30062991.v1
title: Relationship with sex-based anatomical differences to lower extremity injury severity in frontal crashes

description: Multiple studies have demonstrated an increased risk of lower extremity injuries for females in frontal crashes. This study aimed to investigate whether sex-based anatomical differences, as measured on computed tomography (CT) scans of the abdomen and pelvis, contribute to lower extremity injury risk. The Crash Injury Research and Engineering Network (CIREN) database (2017–2023) was queried for frontal collisions. Cases were included if the occupant was an adult (≥18 years), seated in the front row, properly restrained, and had an analyzable CT scan of the abdomen and pelvis. Anatomical measurements included soft tissue over the anterior superior iliac spine (ASIS) along three vectors, thigh diameter, thigh anterior soft tissue, subcutaneous adipose tissue (SAT) area, psoas area, lumbar spine bone mineral density (BMD), femur cortical thickness, height, weight, and body mass index (BMI). Ordinal regression analysis was employed to examine the individual associations between these measurements and the highest Abbreviated Injury Scale (AIS) injury severity of the lower extremities, after adjusting for age, change in velocity (delta-V), knee bolster intrusion, toe pan intrusion, seat track position, airbag deployment, and sex. The same analysis was repeated for the highest AIS injury severity in the hip-thigh-knee and the lower leg regions. A total of 195 occupants were included, of which 114 (58%) were female. Among the anatomical measurements from the computed tomography (CT) scans, significant differences between the sexes were observed only in the SAT and psoas areas, with the SAT area being higher in females and the psoas area being higher in males. Ordinal regression analysis revealed that SAT area, weight, and BMI were significantly associated with the severity of lower extremity injuries. These three variables were also significantly associated with hip-thigh-knee injuries but not with injuries to the lower leg. Further analysis indicated that SAT area is a significant mediator of the effect of sex on lower extremity injury severity. The area of subcutaneous tissue at the level of the ASIS was significantly associated with increased severity of lower extremity injuries, especially in the hip-thigh-knee region. This measurement was higher in females and may account for some of the observed differences in injury risk between sexes.
DOI: 10.6084/m9.figshare.29307154.v1
title: Exploring advanced restraint technologies for improving safety equity for rear-seat occupants using diverse human body models

description: The rising popularity of ridesharing services and the advances of automated driving systems indicate a potential increase in rear-seat occupancy, especially by vulnerable populations, such as the elderly and people with obesity. However, rear-seat safety has not kept pace with the advancements in front-seat safety technologies. This study addresses these challenges by evaluating the effectiveness of advanced restraint systems, including advanced belt features and a self-conforming rear airbag (SCaRAB), in mitigating injury risks for rear-seat occupants during severe frontal crashes. Under a 56 kph frontal crash pulse, we simulated 20 baseline and 320 design variations using 20 morphed finite element (FE) human body models and a validated rear-seat crash environment. The FE human models were mesh morphed from the GHBMC v2.3 simplified midsize male model, representing 10 male and 10 female occupants with a wide range of sizes and shapes. The performance of the advanced restraint systems on reducing the head/brain, chest, lower extremity, and whole-body injury (<i>P<sub>joint</sub></i>) risks was evaluated with varying the belt and airbag designs. Overall, the advanced belt configurations, specifically those with SCaRAB, demonstrated significant whole-body injury risk reduction of up to 42.11% compared to the baseline design. It was observed that shorter obese occupants benefited the most from advanced restraint technologies, while shorter lean occupants benefited the least among the sampled occupants. Trade-offs were observed between head/brain and chest injury risks, as higher belt load limits mitigated head excursion but increased chest deflection, underscoring the complexity of achieving safety equity across a diverse population. This study highlights the potential of advanced restraint technologies to enhance rear-seat occupant protection and address different safety needs across a diverse population.
DOI: 10.6084/m9.figshare.29307154
title: Exploring advanced restraint technologies for improving safety equity for rear-seat occupants using diverse human body models

description: The rising popularity of ridesharing services and the advances of automated driving systems indicate a potential increase in rear-seat occupancy, especially by vulnerable populations, such as the elderly and people with obesity. However, rear-seat safety has not kept pace with the advancements in front-seat safety technologies. This study addresses these challenges by evaluating the effectiveness of advanced restraint systems, including advanced belt features and a self-conforming rear airbag (SCaRAB), in mitigating injury risks for rear-seat occupants during severe frontal crashes. Under a 56 kph frontal crash pulse, we simulated 20 baseline and 320 design variations using 20 morphed finite element (FE) human body models and a validated rear-seat crash environment. The FE human models were mesh morphed from the GHBMC v2.3 simplified midsize male model, representing 10 male and 10 female occupants with a wide range of sizes and shapes. The performance of the advanced restraint systems on reducing the head/brain, chest, lower extremity, and whole-body injury (<i>P<sub>joint</sub></i>) risks was evaluated with varying the belt and airbag designs. Overall, the advanced belt configurations, specifically those with SCaRAB, demonstrated significant whole-body injury risk reduction of up to 42.11% compared to the baseline design. It was observed that shorter obese occupants benefited the most from advanced restraint technologies, while shorter lean occupants benefited the least among the sampled occupants. Trade-offs were observed between head/brain and chest injury risks, as higher belt load limits mitigated head excursion but increased chest deflection, underscoring the complexity of achieving safety equity across a diverse population. This study highlights the potential of advanced restraint technologies to enhance rear-seat occupant protection and address different safety needs across a diverse population.