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Computers in Biology and Medicine Jun 2024Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with...
INTRODUCTION
Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans.
METHODS
A multi-task learning approach was used to segment both bone and BML from T2 fat-suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions.
RESULTS
The 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures.
CONCLUSION
Automated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.
PubMed: 38905892
DOI: 10.1016/j.compbiomed.2024.108791 -
Clinical Imaging Jun 2024Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional... (Review)
Review
BACKGROUND
Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning.
METHODS
A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS).
RESULTS
Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927).
CONCLUSION
Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.
PubMed: 38905878
DOI: 10.1016/j.clinimag.2024.110225 -
Medicine Jun 2024Maintaining a balanced bile acids (BAs) metabolism is essential for lipid and cholesterol metabolism, as well as fat intake and absorption. The development of obesity...
Maintaining a balanced bile acids (BAs) metabolism is essential for lipid and cholesterol metabolism, as well as fat intake and absorption. The development of obesity may be intricately linked to BAs and their conjugated compounds. Our study aims to assess how BAs influence the obesity indicators by Mendelian randomization (MR) analysis. Instrumental variables of 5 BAs were obtained from public genome-wide association study databases, and 8 genome-wide association studies related to obesity indicators were used as outcomes. Causal inference analysis utilized inverse-variance weighted (IVW), weighted median, and MR-Egger methods. Sensitivity analysis involved MR-PRESSO and leave-one-out techniques to detect pleiotropy and outliers. Horizontal pleiotropy and heterogeneity were assessed using the MR-Egger intercept and Cochran Q statistic, respectively. The IVW analysis revealed an odds ratio of 0.94 (95% confidence interval: 0.88, 1.00; P = .05) for the association between glycolithocholate (GLCA) and obesity, indicating a marginal negative causal association. Consistent direction of the estimates obtained from the weighted median and MR-Egger methods was observed in the analysis of the association between GLCA and obesity. Furthermore, the IVW analysis demonstrated a suggestive association between GLCA and trunk fat percentage, with a beta value of -0.014 (95% confidence interval: -0.027, -0.0004; P = .04). Our findings suggest a potential negative causal relationship between GLCA and both obesity and trunk fat percentage, although no association survived corrections for multiple comparisons. These results indicate a trend towards a possible association between BAs and obesity, emphasizing the need for future studies.
Topics: Mendelian Randomization Analysis; Humans; Obesity; Genome-Wide Association Study; Bile Acids and Salts; Causality
PubMed: 38905395
DOI: 10.1097/MD.0000000000038610 -
Frontiers in Genetics 2024Previous studies have shown that Alzheimer's disease (AD) can cause myocardial damage. However, whether there is a causal association between AD and non-ischemic...
The causal effect of Alzheimer's disease and family history of Alzheimer's disease on non-ischemic cardiomyopathy and left ventricular structure and function: a Mendelian randomization study.
BACKGROUND
Previous studies have shown that Alzheimer's disease (AD) can cause myocardial damage. However, whether there is a causal association between AD and non-ischemic cardiomyopathy (NICM) remains unclear. Using a comprehensive two-sample Mendelian randomization (MR) method, we aimed to determine whether AD and family history of AD (FHAD) affect left ventricular (LV) structure and function and lead to NICM, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM).
METHODS
The summary statistics for exposures [AD, paternal history of AD (PH-AD), and maternal history of AD (MH-AD)] and outcomes (NICM, HCM, DCM, and LV traits) were obtained from the large European genome-wide association studies. The causal effects were estimated using inverse variance weighted, MR-Egger, and weighted median methods. Sensitivity analyses were conducted, including Cochran's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outlier, MR Steiger test, leave-one-out analysis, and the funnel plot.
RESULTS
Genetically predicted AD was associated with a lower risk of NICM [odds ratio (OR) 0.9306, 95% confidence interval (CI) 0.8825-0.9813, = 0.0078], DCM (OR 0.8666, 95% CI 0.7752-0.9689, = 0.0119), and LV remodeling index (OR 0.9969, 95% CI 0.9940-0.9998, = 0.0337). Moreover, genetically predicted PH-AD was associated with a decreased risk of NICM (OR 0.8924, 95% CI 0.8332-0.9557, = 0.0011). MH-AD was also strongly associated with a decreased risk of NICM (OR 0.8958, 95% CI 0.8449-0.9498, = 0.0002). Different methods of sensitivity analysis demonstrated the robustness of the results.
CONCLUSION
Our study revealed that AD and FHAD were associated with a decreased risk of NICM, providing a new genetic perspective on the pathogenesis of NICM.
PubMed: 38903751
DOI: 10.3389/fgene.2024.1379865 -
BMC Women's Health Jun 2024Previous observational studies have indicated an inverse correlation between circulating sex hormone binding globulin (SHBG) levels and the incidence of polycystic ovary...
BACKGROUND
Previous observational studies have indicated an inverse correlation between circulating sex hormone binding globulin (SHBG) levels and the incidence of polycystic ovary syndrome (PCOS). Nevertheless, conventional observational studies may be susceptible to bias. Consequently, we conducted a two-sample Mendelian randomization (MR) investigation to delve deeper into the connection between SHBG levels and the risk of PCOS.
METHODS
We employed single-nucleotide polymorphisms (SNPs) linked to serum SHBG levels as instrumental variables (IVs). Genetic associations with PCOS were derived from a meta-analysis of GWAS data. Our primary analytical approach relied on the inverse-variance weighted (IVW) method, complemented by alternative MR techniques, including simple-median, weighted-median, MR-Egger regression, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) testing. Additionally, sensitivity analyses were conducted to assess the robustness of the association.
RESULTS
We utilized 289 SNPs associated with serum SHBG levels, achieving genome-wide significance, as instrumental variables (IVs). Our MR analyses revealed that genetically predicted elevated circulating SHBG concentrations were linked to a reduced risk of PCOS (odds ratio (OR) = 0.56, 95% confidence interval (CI): 0.39-0.78, P = 8.30 × 10) using the IVW method. MR-Egger regression did not detect any directional pleiotropic effects (P intercept = 0.626). Sensitivity analyses, employing alternative MR methods and IV sets, consistently reaffirmed our results, underscoring the robustness of our findings.
CONCLUSIONS
Through a genetic epidemiological approach, we have substantiated prior observational literature, indicating a potential causal inverse relationship between serum SHBG concentrations and PCOS risk. Nevertheless, further research is needed to elucidate the underlying mechanism of SHBG in the development of PCOS.
Topics: Humans; Sex Hormone-Binding Globulin; Polycystic Ovary Syndrome; Female; Polymorphism, Single Nucleotide; Mendelian Randomization Analysis; Genome-Wide Association Study; Genetic Predisposition to Disease; Risk Factors
PubMed: 38902677
DOI: 10.1186/s12905-024-03144-6 -
Wiener Klinische Wochenschrift Jun 2024To investigate the genetic level causal association among hyperthyroidism, hypothyroidism, and rheumatoid arthritis (RA).
OBJECTIVE
To investigate the genetic level causal association among hyperthyroidism, hypothyroidism, and rheumatoid arthritis (RA).
METHODS
We utilized the genome-wide association studies (GWAS) summary data for exposure (hyperthyroidism and hypothyroidism) and outcome (RA) from the IEU OpenGWAS database. We used two different sets of data (test cohort and validation cohort) for causal assessment of exposure and outcome. To establish a causal relationship between these conditions, we conducted a two-sample Mendelian randomization (MR) analysis. Subsequently, we evaluated the MR analysis results for heterogeneity, horizontal pleiotropy, and outliers, aiming to assess the validity and reliability of the findings. Moreover, we conducted additional analyses to examine the robustness of the MR results, including a "Leave one out" analysis and the MR robust adjusted profile score (MR-RAPS) method, ensuring the robustness and adherence to normal distribution assumptions.
RESULTS
The findings from the test cohort indicated that hyperthyroidism did not exhibit a genetic causal association with RA (P = 0.702, odds ratio [OR] 95% confidence interval [CI] = 1.021 [0.918-1.135]). Conversely, hypothyroidism displayed a positive genetic causal relationship with RA (P < 0.001, OR 95% CI = 1.239 [1.140-1.347]). The analysis results of the validation cohort are consistent with those of the test cohort. Notably, our MR analysis results demonstrated no evidence of heterogeneity, horizontal pleiotropy, or outliers. Furthermore, our MR analysis results remained unaffected by any single nucleotide polymorphism (SNP) and exhibited a normal distribution.
CONCLUSION
The results of this study showed that hypothyroidism was positively correlated with RA, while hyperthyroidism was not causally correlated with RA. Hypothyroidism may as a risk factor of RA should be paid attention to in clinical work. Future studies are needed to further confirm this finding.
PubMed: 38902562
DOI: 10.1007/s00508-024-02386-6 -
Clinical Nutrition ESPEN Aug 2024The emerging role of vitamin D has drawn the attention of researchers around the world, including its involvement in cardiovascular complications among individuals with... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The emerging role of vitamin D has drawn the attention of researchers around the world, including its involvement in cardiovascular complications among individuals with diabetes.
AIM
This study aimed to obtain comprehensive evidence on the association between serum vitamin D level and the risk of cardiovascular disease among patients with diabetes.
METHODS
Systematic search was performed on July 1st, 2023, to identify and screen published literature reporting the association between vitamin D and cardiovascular disease among diabetic patients in six databases. Each eligible study was appraised for its quality using modified Newcastle Ottawa Scale for cross-sectional and cohort studies. Meta-analysis was performed using Dersimonian-Laird random effect model or fix-effect model. The heterogeneity and publication bias were judged based on percentage of I and the symmetry of Begg's funnel plot, respectively.
RESULTS
As many as 22 studies were found eligible for the systematic review. A meta-analysis from 13 studies comprising of 3850 and 1797 (control and exposure groups, respectively) revealed that serum vitamin D level was significantly lower in patients with diabetes and cardiovascular diseases (Z = 4.89; p-total<0.001; SMD = 0.68 [95%CI: 0.41-0.95]), yet the heterogeneity was high. Following the adjustment of removing the potential outliers, the same results were still observed (Z = 6.19; p-total<0.001; SMD = 0.35 [95%CI: 0.24-0.46]). Though decreased, high heterogeneity could not be resolved, resulting in moderate level of this evidence. Another pooled analysis of 7 studies with 4211 patients in control group and 2381 patients in exposure group revealed that lower level of serum vitamin D is a risk factor for cardiovascular disease incidence among diabetic patients (Z = 4.89; p-total<0.001; OR: 1.76 [95%CI: 1.4-2.2]).
CONCLUSION
Serum vitamin D level status is a risk factor for developing cardiovascular diseases among diabetic patients, hence should be carefully monitored and maintained.
PROSPERO REGISTRATION
CRD42023437698.
Topics: Humans; Cardiovascular Diseases; Vitamin D; Risk Factors; Vitamin D Deficiency; Diabetes Mellitus; Cross-Sectional Studies
PubMed: 38901950
DOI: 10.1016/j.clnesp.2024.04.018 -
Traffic Injury Prevention Jun 2024Injury outcomes for powered two- and three-wheeler (PTW) riders are influenced by the rider posture. To enable analysis of PTW rider accidents and development of...
OBJECTIVE
Injury outcomes for powered two- and three-wheeler (PTW) riders are influenced by the rider posture. To enable analysis of PTW rider accidents and development of protection systems, detailed whole-body posture data is needed. Therefore, the aim of this study is to fill this gap by providing collections of average male whole-body postures, including subpopulation variability, for different PTW types. This will enable future studies to explore the influence of PTW rider posture variation and to support safety system development.
METHODS
3D photometric measurements of 51 anatomical landmarks were recorded on 20 (50th percentile male) volunteers in their preferred riding postures across three PTW types (naked, scooter, and touring). Following an outlier removal process, a principal component analysis (PCA) was performed to calculate average postures and principal components (PCs), to describe the observed posture variation, for each PTW. The visualization of the PCs was facilitated through kinematic linkage representations, connecting anatomical landmarks and estimated joint centers to form segments and characteristic joint angles.
RESULTS
The first seven PCs explained 80% of the variance in posture for each of the three PTWs. Across PTWs, these PCs frequently described combinations of postural features including variation in fore-aft seat positions, pelvic tilt, spinal curvature, head position, and extremity flexion-extension. Analysis revealed distinct differences in average postures across the three PTWs, on average, 10 ± 9° for the characteristic joint angles within a min-to-max range between the three PTWs. However, for all three PTWs, the variability between volunteers in characteristic joint angles on the same PTW were on average more than twice as large within a ± 2 SD range (26 ± 11°).
CONCLUSIONS
The results suggest that PTW rider posture variation must be addressed by involving simultaneous adjustments of multiple body parts, as described by each of the first seven PCs, as a direct consequence of the human body interconnectedness. Furthermore, the study's findings challenge conventional assumptions that the relative distance between PTWs' handlebar, seat, and foot support predominantly influences rider postures. Instead, the research demonstrates that individual variability has a substantial influence on rider posture and should be considered in PTW safety development.
PubMed: 38900933
DOI: 10.1080/15389588.2024.2351607 -
Pediatric Blood & Cancer Jun 2024Adolescent venous thromboembolism (VTE) has unique challenges in management, complications, and compliance to anticoagulants. Direct oral anticoagulants (DOACs) have... (Review)
Review
Adolescent venous thromboembolism (VTE) has unique challenges in management, complications, and compliance to anticoagulants. Direct oral anticoagulants (DOACs) have been approved for pediatric VTE management, with an increasing use especially in adolescents. Primary objective is to evaluate the safety and efficacy of DOAC therapy in adolescent VTE. Secondary objectives include adverse events, bleeding events, and overall mortality. A SR protocol was registered in PROSPERO 2022 (CRD42022363928). Databases were searched from inception to September 22, 2022. Studies with children aged 10-18 years, VTE diagnosis, DOAC therapy, randomized control trials (RCTs), cohort, and relevant study types were included. Studies including prophylaxis, non-DOAC therapy, arterial thrombosis, age outliers, non-relevant study types were excluded. Findings are reported in accordance to PRISMA 2020. Nine reports from five studies, published between 2016 and 2022, were included. Rivaroxaban was the most common DOAC. VTE recurrence was 0.02% in the rivaroxaban phase III trial and one patient in the dabigatran phase IIb/III trial. Complete/partial thrombus resolution (CR/PR) was 76.6% in the rivaroxaban phase III trial, and 83.9% in the dabigatran phase IIb/III trial. CR/PR was found to be 68.4% in Dhaliwal et al. study and 83.3% in Hassan et al. study. Major bleeding occurred in one patient. Headache and gastrointestinal symptoms were commonly seen. All-cause mortality occurred in a patient due to cancer progression. DOAC therapy in adolescent VTE had CR/PR in two-thirds of the patients, with low incidence of VTE recurrence and major bleeding. As there are only two randomized controlled trial (RCTs), future adolescents' studies are required to validate our results.
PubMed: 38899913
DOI: 10.1002/pbc.31131 -
Statistics in Medicine Jun 2024Meta-analysis is an essential tool to comprehensively synthesize and quantitatively evaluate results of multiple clinical studies in evidence-based medicine. In many...
Meta-analysis is an essential tool to comprehensively synthesize and quantitatively evaluate results of multiple clinical studies in evidence-based medicine. In many meta-analyses, the characteristics of some studies might markedly differ from those of the others, and these outlying studies can generate biases and potentially yield misleading results. In this article, we provide effective robust statistical inference methods using generalized likelihoods based on the density power divergence. The robust inference methods are designed to adjust the influences of outliers through the use of modified estimating equations based on a robust criterion, even when multiple and serious influential outliers are present. We provide the robust estimators, statistical tests, and confidence intervals via the generalized likelihoods for the fixed-effect and random-effects models of meta-analysis. We also assess the contribution rates of individual studies to the robust overall estimators that indicate how the influences of outlying studies are adjusted. Through simulations and applications to two recently published systematic reviews, we demonstrate that the overall conclusions and interpretations of meta-analyses can be markedly changed if the robust inference methods are applied and that only the conventional inference methods might produce misleading evidence. These methods would be recommended to be used at least as a sensitivity analysis method in the practice of meta-analysis. We have also developed an R package, robustmeta, that implements the robust inference methods.
PubMed: 38899515
DOI: 10.1002/sim.10157