62 research outputs found
Global burden of injury due to low bone mineral density in adults aged 55 years and older, 1990 to 2021: A population-based study
No full textObjectives: This study aimed to assess the global burden of injuries due to low bone mineral density (BMD) among adults aged 55 and above from 1990 to 2021, focusing on mortality and disability-adjusted life years (DALYs) and analyzing trends across sexes, age groups, and sociodemographic index (SDI) regions.
Methods: Data from the Global Burden of Disease Study 2021, covering 204 countries and territories, were analyzed. Joinpoint regression quantified temporal changes in mortality and DALYs, calculating average annual percentage change (AAPC). Age-period-cohort modeling elucidated demographic influences, and decomposition analysis identified key contributors to mortality changes.
Results: Globally, in 2021, the crude DALY rate for injuries due to low BMD was 900.32 (95 % UI: 742.64 to 1081.51) per 100,000, and the crude mortality rate was 27.04 (95 % UI: 22.49 to 30.75) per 100,000. The agestandardized mortality rate for injuries due to low BMD showed no significant change from 1990 to 2021 (AAPC 0.26 %, P = 0.071), but there was a significant increase in countries with a high SDI (AAPC 0.51 %, P = 0.001).
The burden of disease in persons aged 80 years and older remained substantial, with a slight increase. Decomposition analysis identified population growth as the main driver of increasing mortality and DALYs.
Conclusion: Despite the reductions in DALY rates, the mortality has remained stable worldwide; however, has risen significantly in high SDI countries. The substantial and slightly increasing burden of disease in people aged 80 years and older underscores the need for targeted strategies for the prevention and management of low BMD to mitigate the future global impact of these injuries.Objectives: This study aimed to assess the global burden of injuries due to low bone mineral density (BMD) among adults aged 55 and above from 1990 to 2021, focusing on mortality and disability-adjusted life years (DALYs) and analyzing trends across sexes, age groups, and sociodemographic index (SDI) regions. Methods: Data from the Global Burden of Disease Study 2021, covering 204 countries and territories, were analyzed. Joinpoint regression quantified temporal changes in mortality and DALYs, calculating average annual percentage change (AAPC). Age-period-cohort modeling elucidated demographic influences, and decomposition analysis identified key contributors to mortality changes. Results: Globally, in 2021, the crude DALY rate for injuries due to low BMD was 900.32 (95 % UI: 742.64 to 1081.51) per 100,000, and the crude mortality rate was 27.04 (95 % UI: 22.49 to 30.75) per 100,000. The age-standardized mortality rate for injuries due to low BMD showed no significant change from 1990 to 2021 (AAPC 0.26 %, P = 0.071), but there was a significant increase in countries with a high SDI (AAPC 0.51 %, P = 0.001). The burden of disease in persons aged 80 years and older remained substantial, with a slight increase. Decomposition analysis identified population growth as the main driver of increasing mortality and DALYs. Conclusion: Despite the reductions in DALY rates, the mortality has remained stable worldwide; however, has risen significantly in high SDI countries. The substantial and slightly increasing burden of disease in people aged 80 years and older underscores the need for targeted strategies for the prevention and management of low BMD to mitigate the future global impact of these injuries
Pioneering a chick embryo model to explore the intrauterine etiology of developmental dysplasia of the hip in oligohydramnios conditions
No full textObjective: To explore the impact of oligohydramnios on fetal movement and hip development, given its association with developmental dysplasia of the hip (DDH) but unclear mechanisms.
Methods: Chick embryos were divided into four groups based on the severity of oligohydramnios induced by amniotic fluid aspiration (control, 0.2 mL, 0.4 mL, 0.6 mL). Fetal movement was assessed by detection of movement and quantification of residual amniotic fluid volume. Hip joint development was assessed by gross anatomic analysis, micro-computed tomography (micro-CT) for cartilage assessment, and histologic
observation at multiple time points. In addition, a subset of embryos from the 0.4 mL aspirated group underwent saline reinfusion and subsequent evaluation.
Results: Increasing volumes of aspirated amniotic fluid resulted in worsening of fetal movement restrictions (e.g., 0.4 mL aspirated and control group at E10: frequency difference −7.765 [95% CI: −9.125, −6.404]; amplitude difference −0.343 [95% CI: −0.588, −0.097]). The 0.4 mL aspirated group had significantly smaller hip measurements compared to controls, with reduced acetabular length (−0.418 [95% CI: −0.575, −0.261])
and width (−0.304 [95% CI: −0.491, −0.117]) at day E14.5. Histological analysis revealed a smaller femoral head (1.084 ± 0.264 cm) and shallower acetabulum (0.380 ± 0.106 cm) in the 0.4 mL group. Micro-CT showed cartilage matrix degeneration (13.6% [95% CI: 0.6%, 26.7%], P = 0.043 on E14.5). Saline reinfusion resulted in significant improvements in the femoral head to greater trochanter (0.578 [95% CI: 0.323, 0.833], P = 0.001).
Conclusions: Oligohydramnios can cause DDH by restricting fetal movement and disrupting hip morphogenesis in a time-dependent manner. Timely reversal of oligohydramnios during the fetal period may prevent DDH.
© 2024 Osteoarthritis Research Society International. Published by Elsevier Ltd.Objective: To explore the impact of oligohydramnios on fetal movement and hip development, given its association with developmental dysplasia of the hip (DDH) but unclear mechanisms. Methods: Chick embryos were divided into four groups based on the severity of oligohydramnios induced by amniotic fluid aspiration (control, 0.2 mL, 0.4 mL, 0.6 mL). Fetal movement was assessed by detection of movement and quantification of residual amniotic fluid volume. Hip joint development was assessed by gross anatomic analysis, micro-computed tomography (micro-CT) for cartilage assessment, and histologic observation at multiple time points. In addition, a subset of embryos from the 0.4 mL aspirated group underwent saline reinfusion and subsequent evaluation. Results: Increasing volumes of aspirated amniotic fluid resulted in worsening of fetal movement restrictions (e.g., 0.4 mL aspirated and control group at E10: frequency difference −7.765 [95% CI: −9.125, −6.404]; amplitude difference −0.343 [95% CI: −0.588, −0.097]). The 0.4 mL aspirated group had significantly smaller hip measurements compared to controls, with reduced acetabular length (−0.418 [95% CI: −0.575, −0.261]) and width (−0.304 [95% CI: −0.491, −0.117]) at day E14.5. Histological analysis revealed a smaller femoral head (1.084 ± 0.264 cm) and shallower acetabulum (0.380 ± 0.106 cm) in the 0.4 mL group. Micro-CT showed cartilage matrix degeneration (13.6% [95% CI: 0.6%, 26.7%], P = 0.043 on E14.5). Saline reinfusion resulted in significant improvements in the femoral head to greater trochanter (0.578 [95% CI: 0.323, 0.833], P = 0.001). Conclusions: Oligohydramnios can cause DDH by restricting fetal movement and disrupting hip morphogenesis in a time-dependent manner. Timely reversal of oligohydramnios during the fetal period may prevent DDH
Towards robust and effective shape prior modeling: sparse shape composition
No full textOrgan shape plays an important role in many clinical practices, including diagnosis, surgical planning and treatment evaluation. It is usually derived from medical images using low level appearance cues. However, due to diseases and imaging artifacts, low level appearance cues are often weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived from image appearances. Effective modeling of shape priors is challenging because: 1) shape variations are complex and cannot always be modeled by parametric probability distributions; 2) a shape instance derived from image appearance cues (called an input shape) may have significant errors; and 3) local details of an input shape may be important for clinical purposes but difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to address these three challenges in a unified framework. With our method, a sparse set of shapes is selected from the shape repository and composed together to infer and refine an input shape. This way, the prior information is implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: 1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; 2) parts of the input shape may contain large errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using norm relaxation, it can be solved by an efficient expectation-maximization (EM) framework. Furthermore, this model is extended to effectively handle multi-resolution, local shape priors and hierarchical priors. We also propose a framework to generate high quality training data in 3D. Our framework includes geometry processing methods and shape registration algorithms. The proposed shape prior model is extensively validated on five different medical applications: 2D lung localization in chest X-ray images, 3D liver segmentation in low-dose Computed Tomography (CT) scans, 3D segmentation of multiple rodent brain structures in Magnetic Resonance (MR) microscope, real time tracking of left ventricles in Magnetic Resonance Imaging (MRI), and high resolution CT reconstruction. Compared to state-of-the-art methods, our model exhibits better performance in all these studies.Ph. D.Includes bibliographical referencesIncludes vitaby Shaoting Zhan
Causal Link between Gut Microbiota, Neurophysiological States, and Bone Diseases: A Comprehensive Mendelian Randomization Study
No full textIncreasing evidence highlights a robust correlation between the gut microbiota and bone diseases; however, the existence of a causal relationship between them remains unclear. In this study, we thoroughly examined the correlation between gut microbiota and skeletal diseases using genome-wide association studies. Linkage disequilibrium score regression and Mendelian randomization were used to probe genetic causality. Furthermore, the potential mediating role of neuropsychological states (i.e., cognition, depression, and insomnia) between the gut microbiota and bone diseases was evaluated using mediation analysis, with genetic colocalization analysis revealing potential targets. These findings suggest a direct causal relationship between Ruminococcaceae and knee osteoarthritis (OA), which appears to be mediated by cognitive performance and insomnia. Similarly, a causal association was observed between Burkholderiales and lumbar pelvic fractures, mediated by cognitive performance. Colocalization analysis identified a shared causal variant (rs2352974) at the TRAF-interacting protein locus for cognitive ability and knee OA. This study provides compelling evidence that alterations in the gut microbiota can enhance cognitive ability, ameliorate insomnia, and potentially reduce the risk of site-specific fractures and OA. Therefore, strategies targeting gut microbiota optimization could serve as novel and effective preventive measures against fractures and OA
Revolutionizing pediatric orthopedics: GPT-4, a groundbreaking innovation or just a fleeting trend?
No full textInterannual and seasonal variability of glacier surface velocity in the parlung zangbo basin, tibetan plateau
No full textMonitoring glacier flow is vital to understand the response of mountain glaciers to environmental forcing in the context of global climate change. Seasonal and interannual variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted significant attention. Detailed patterns in glacier surface velocity and its seasonal variability in the PZB are still uncertain, however. We utilized Landsat-8 (L8) OLI data to investigate in detail the variability of glacier velocity in the PZB by applying the normalized image cross-correlation method. On the basis of satellite images acquired from 2013 to 2020, we present a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Such increase likely had an impact on ice mass accumulation in the up-stream portion of the glacier. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. Besides, there was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual velocity variability was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. Furthermore, the variations in glacier surface velocity are likely related to topographic setting and basal slip caused by the percolation of rainfall. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The reasons that influence the seasonal surface velocity change need further investigation.Optical and Laser Remote Sensin
Changes in glacier albedo and the driving factors in the Western Nyainqentanglha Mountains from 2001 to 2020
No full textGlacier surface albedo dominates glacier energy balance, thus strongly affecting the glacier mass balance. Glaciers in the Western Nyainqentanglha Mountains (WNM) experienced large mass losses in the past two decades, but long-term changes of glacier albedo and its drivers are less understood. In this study, we retrieved glacier albedo with MODIS reflectance data to characterize the spatiotemporal variability of albedo from 2001 to 2020. Air temperature, rainfall, snowfall and deposition of light-absorbing impurities (LAIs) were evaluated as potential drivers of the observed variability in glacier albedo. The results showed that: (1) the glacier albedo experienced large inter-annual fluctuations, with the mean albedo being 0.552 ± 0.002 and a clear decreasing trend of 0.0443 ± 2 × 10-4 dec-1 in the WNM. The fastest decline was observed in autumn and in the vicinity of the equilibrium line altitude, indicating an extended melt season and an expansion of the ablation region to higher elevation; (2) local meteorology and LAIs deposition are the main drivers of glacier albedo change, but their effects on seasonal albedos are different due to different glacier processes. Both air temperature and the balance between liquid and solid precipitation affect summer and autumn albedos due to glacier ablation. Air temperature is the main driver of spring and winter albedos due to sublimation and metamorphism of snow, while snowfall carried by westerlies has limited influence on these two seasonal albedos due to less snowfall. LAIs mainly affect spring albedo due to high concentration coupled with the southerly wind in spring. These findings highlight the significance of changes in glacier albedo and the key role of local meteorology and LAIs deposition in determining such changes, which play an important role in glaciological and cryosphere processes. Optical and Laser Remote Sensin
Glacier area and snow cover changes in the range system surrounding tarim from 2000 to 2020 using google earth engine
No full textGlacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.Optical and Laser Remote Sensin
SegRap2023 : A benchmark of organs-at-risk and gross tumor volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
No full textRadiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Despite that deep learning has achieved remarkable performance on various medical image segmentation tasks, its performance on OARs and GTVs of NPC is still limited, and high-quality benchmark datasets on this task are highly desirable for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge aimed to segment 45 OARs and 2 GTVs from the paired CT scans per patient, and received 10 and 11 complete submissions for the two tasks, respectively. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68% to 86.70%, and 70.42% to 73.44% for OARs and GTVs, respectively. We conclude that the segmentation of relatively large OARs is well-addressed, and more efforts are needed for GTVs and small or thin OARs. The benchmark remains available at: https://segrap2023.grand-challenge.org.</p
Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases
No full textDeep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and complex organs remains a challenging open problem for clinical adoption. To investigate model robustness, we collected and annotated the RAOS dataset comprising 413 CT scans (80k 2D images, 8k 3D organ annotations) from 413 patients each with 17 (female) or 19 (male) labelled organs, manually delineated by oncologists. We grouped scans based on clinical information into 1) diagnosis/radiotherapy (317 volumes), 2) partial excision without the whole organ missing (22 volumes), and 3) excision with the whole organ missing (74 volumes). RAOS provides a potential benchmark for evaluating model robustness including organ hallucination. It also includes some organs that can be very hard to access on public datasets like the rectum, colon, intestine, prostate and seminal vesicles. We benchmarked several state-of-the-art methods in these three clinical groups to evaluate performance and robustness. We also assessed cross-generalization between RAOS and three public datasets. This dataset and comprehensive analysis establish a potential baseline for future robustness research: \url{https://github.com/Luoxd1996/RAOS}.10 pages, 1 figure, 6 tables, Early Accept to MICCAI 202
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