Browse Articles

Global burden of thyroid cancer in adolescents and young adults (aged 15–39 years) from 1990 to 2021: A systematic analysis of the Global Burden of Disease Study 2021

Background Thyroid cancer (TC) is the most common malignancy of the endocrine system and head-and-neck region, yet data on its burden in adolescents and young adults (AYAs) is lacking. This study aimed to estimate the global burden of TC among AYAs from 1990 to 2021. Methods Utilizing the Global Burden of Disease (GBD) 2021 data, we analyzed age-standardized rates of incidence, prevalence, and disability-adjusted life-years (DALYs) on global, regional, and national scales. Joinpoint regression was employed to determine average annual percentage change (AAPC), with frontier analysis revealing regions for improvement. Decomposition analysis assessed the impacts of population aging, growth, and epidemiological changes. Projections for disease burden extending to 2040 were generated using the Bayesian Age-Period-Cohort model. Result In 2021, there were 48.2 thousand incident cases, 436.1 thousand prevalent cases, and 183.5 thousand DALYs worldwide. Meantime, the age-standardized incidence rates (ASIR), age-standardized prevalence rates (ASPR), and age-standardized DALYs rates (ASDR) were 1.6, 14.3 and 6.1 per 100 000, respectively. From 1990 to 2021, the ASIR, ASPR and ASDR increased with AAPCs of 1.73, 1.77, and 0.38, respectively. Socio-demographic resources in Saudi Arabia, Taiwan (Province of China), Iceland, United Arab Emirates, and United States Virgin Islands have the potential to lower ASDR due to TC among AYAs. Furthermore, 13.3 thousand and 34.9 thousand new cases occurred in the males and females in 2021. Among 5 age groups, the highest numbers of incidence, prevalence, and DALYs, along with ASRs, were observed in the 35–39 age group. Global projections indicated a continuous rise in numbers of incidence, prevalence, and DALYs, with estimates of 60.2 thousand, 558.4 thousand, and 199.7 thousand by 2040, respectively. Conclusion The global burden of TC among AYAs was on the rise, with significant disparities by regions, genders, and age groups, highlighting the necessity for targeted and effective interventions.

Therapeutic effect of curcumin derivative GT863 on prion-infected mice

Combining 3D printing technology with customized metal plates for the treatment of complex acetabular fractures: A retrospective study

Purposes The purpose of this study is to evaluate the clinical outcomes of combining 3D printing technology with customized metal plates in the treatment of complex acetabular fractures. Methods A retrospective study was conducted on 42 patients with complex acetabular fractures treated at our hospital between September 1, 2020 and May 31, 2022. The patients were divided into two groups: the conventional group and the 3D printing group, with 21 individuals in each group.The conventional group underwent surgery using traditional surgical techniques, with appropriate bending and adjustment of the metal plates during the procedure. In the 3D printing group, preoperative 3D printing technology was utilized to create a physical model of the fracture, and individualized metal plates were customized based on the model after virtual reduction. Comparison was made between the two groups of patients regarding surgical approach, operative time, instrument handling time, intraoperative blood loss, number of fluoroscopy scans, fracture healing time, quality of fracture reduction postoperatively, hip joint function at 12 months postoperatively, and occurrence of complications during the follow-up period. Results The 3D printing group showed significantly shorter surgical time (124.76±12.89 minutes vs. 174.05±12.51 minutes), instrument operation time (44.57±5.32 minutes vs. 62.9±7.47 minutes), intraoperative blood loss (337.38±51.95 mL vs. 545.24±74.39 mL), and intraoperative fluoroscopy frequency (8.25±1.18 times vs. 10.52±1.6 times) compared to the conventional group (P<0.001). The postoperative fracture reduction quality in the 3D printing group was good in 95.24% (20/21) of cases, significantly higher than the 61.90% (13/21) in the conventional group (P = 0.02). The excellent and good hip function rate in the 3D printing group was 90.48% (19/21), which was also significantly higher than 57.14% (12/21) in the conventional group (P = 0.01). No significant difference was observed between the two groups in fracture healing time (13.95±1.07 weeks vs. 13.81±1.17 weeks) and complication rate (9.52% vs. 28.57%) (P = 0.14; P = 0.24). Conclusion The application of 3D printing technology in conjunction with individualized customization of metal plates for the treatment of complex acetabular fractures can shorten surgical and instrument handling time, reduce intraoperative blood loss, and improve the quality of fracture reduction as well as the recovery of hip joint function.These results provide new insights and technical support for the treatment of complex acetabular fractures.

Machine learning tools match physician accuracy in multilingual text annotation

Does corporate governance mechanism deter earnings management and enhance readability of annual reports?

This study aims to know the impact of earnings management (accrual, real and total) and corporate governance mechanisms on the readability of annual reports. Additionally, the study also seeks to know the moderating impact of corporate governance mechanisms between earnings management (accruals, real and total) and readability of annual reports. The sample of the study consists 250 listed firms of Pakistan Stock Exchange (PSX) for the period of 2014–2022. The hypotheses are tested using System GMM technique. The results of the study suggest that earnings management (accruals, real and total) has negative and significant impact on readability of annual reports. However, all four corporate governance mechanics have a significantly positive impact on readability of annual reports. Additionally, corporate governance mechanics significantly moderates the relationship between earnings management (accruals, real and total) and the readability of annual reports. This study has practical implications for regulators, investors, and firms. The findings of the study may suggest to the Security Exchange Commission Pakistan that authorities improve readability by requiring companies to use clear, understandable language and include appropriate information in annual reports. Firms listed on PSX need to produce more readable annual reports to make information more concise and clear, using simple and short sentences, and familiar words.

Correlation between physiological and biochemical variables during short term adequate protein intake combined with resistance exercise in sedentary adults

SC-GROG followed by L+S reconstruction with multiple sparsity constraints for accelerated Golden-angle-radial DCE-MRI

The GRASP (Golden-angle-radial Sparse Parallel MRI) is a contemporary method for reconstructing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This method combines the temporal incoherence of stack-of-stars Golden-angle-radial sampling pattern and acceleration capability of parallel MRI (PI) and compressed sensing (CS) for highly accelerated free-breathing DCE-MRI reconstruction. GRASP uses Temporal Total Variation (TV) norm as a sparsity transform to promote sparsity among multi-coil MRI data and Nonlinear Conjugate Gradient (NL-CG) algorithm to obtain an optimal solution. Additionally, GRASP uses NUFFT gridding to map Golden-angle-radial data to Cartesian grid before NL-CG based CS reconstruction. However, major limitations of GRASP include the temporal averaging effect due to Temporal TV, leading to a degradation in the dynamic contrast of DCE-MRI, and a high computational burden/reconstruction time due to repeated NUFFT gridding/degridding in NL-CG reconstruction. This paper introduces a novel approach to address limitations in GRASP reconstruction technique for free-breathing DCE-MRI. The proposed method combines SC-GROG gridding with low-rank plus sparse (L+S) reconstruction using multiple sparsity constraints for accelerated Golden-angle-radial DCE-MRI with improved temporal resolution and dynamic contrast. Monotone FISTA with variable acceleration (MFISTA-VA) is used to optimize the L+S optimization problem. Further, SC-GROG gridding is used to map Golden-angle radial data to Cartesian grid before L+S reconstruction. The proposed method is tested on two different 3T free-breathing in-vivo DCE-MRI datasets. Reconstruction results of the proposed method are evaluated by using: (i) convergence error, (ii) peak and mean values of arterial signal intensity in the selected region of interest (ROI) of DCE MR Images, and (iii) reconstruction time. Results show that the proposed method provides significant improvements in the reconstruction time and dynamic contrast than the conventional Golden-angle-radial DCE-MRI reconstruction techniques (i.e., GRASP, XD-GRASP). Furthermore, convergence analysis shows that integration of MFISTA-VA in L+S reconstruction provides faster convergence compared to conventional L+S reconstruction.

Tracing the Neovolcanic zone along the sediment-covered regions of the Red Sea Rift

Abstract The Red Sea Rift is an ultra-slow spreading rift filled with Miocene salt and younger sediments. While volcanic features can be observed in exposed areas in the southern Red Sea Rift, evidence of volcanism in the sediment-blanketed regions in the central and northern Red Sea Rift has been lacking, leaving open whether the mid-ocean rift axis continues beneath them. Here, we present new multichannel seismic and high-resolution bathymetric data of these blanketed regions. Our data reveals multiple instances where oceanic crust can be traced beneath the evaporite cover, forming volcanic edifices protruding through the sediment cover. We identify abundant circular depressions in the sediment cover as volcanic craters, which formed by deep-sea explosive volcanism or caldera collapses. The common occurrence of volcanic features in the sediment-covered regions supports the continuous formation of oceanic crust along large parts of the Red Sea Rift.

Publication bias in the social sciences since 1959: Application of a regression discontinuity framework

While publication bias has been widely documented in the social sciences, it is unclear whether the problem aggravated over the last decades due to an increasing pressure to publish. We provide an in-depth analysis of publication bias over time by creating a unique data set, consisting of 12340 test statistics extracted from 571 papers published in 1959-2018 in the Quarterly Journal of Economics. We, further, develop a new methodology to test for discontinuities at the thresholds of significance. Our findings reveal, that, first, in contrast to our expectations, publication bias was already present many decades ago, but that, second, bias patterns notably changed over time. As such, we observe a transition from bias at the 10 percent to bias at the 5 percent significance level. We conclude that these changes are influenced by increasing computational possibilities as well as changes in the acceptance rates of scientific top journals.

Anisotropy visualisation from X-ray diffraction of biological apatite in mixed phase calcified tissue samples

Abstract X-ray diffraction is widely used to characterise the mineral component of calcified tissue. Broadening of the diffraction peaks yields valuable information on the size of coherently diffracting domains, sometimes loosely described as crystallite size or crystallinity. These domains are markedly anisotropic, hence a single number describing their size is misleading. We present a novel variation on a method for visualising crystallographic anisotropy in X-ray diffraction data. This provides an intuitively interpretable depiction of crystalline domain size and anisotropy. The new method involves creating a polar plot of calculated domain thickness for peaks in a diffractogram versus crystallographic direction. Points with the least error are emphasised. Anisotropic domain dimensions are calculated by refining an ellipsoidal model in a whole pattern fit. These dimensions are then used to overlay an ellipse on the peak broadening plot. This is illustrated by application of the method to calcifications in breast tissue with suspected cancer, which frequently contain whitlockite as well as nanocrystalline apatite. Like most biogenic apatite, this exhibits markedly anisotropic peak broadening. The nature of this anisotropy offers potentially useful information on normal function and pathology of calcified tissue and is a frequently neglected crystallographic feature of these materials.

Establishing the effect of computed tomography reconstruction kernels on the measure of bone mineral density in opportunistic osteoporosis screening

Pure data correction enhancing remote sensing image classification with a lightweight ensemble model

A randomized trial comparing medium cut-off membrane dialyzers with online hemodiafiltration for uremic toxins clearance in hemodialysis patients

Accelerating inverse Kohn–Sham calculations using reduced density matrices

The Ryabinkin–Kohut–Staroverov (RKS) and Kanungo–Zimmerman–Gavini (KZG) methods offer two approaches to find exchange-correlation (XC) potentials from ground state densities. The RKS method utilizes the one- and two-particle reduced density matrices to alleviate any numerical artifacts stemming from a finite basis (e.g., Gaussian- or Slater-type orbitals). The KZG approach relies solely on the density to find the XC potential by combining a systematically convergent finite-element basis with appropriate asymptotic correction on the target density. The RKS method, being designed for a finite basis, offers computational efficiency. The KZG method, using a complete basis, provides higher accuracy. In this work, we combine both methods to simultaneously afford accuracy and efficiency. In particular, we use the RKS solution as an initial guess for the KZG method to attain a significant 3–11× speedup. This work also presents a direct comparison of the XC potentials from the RKS and the KZG method and their relative accuracy on various weakly and strongly correlated molecules, using their ground state solutions from accurate configuration interaction calculations solved in a Slater orbital basis.

A parallel texture-based region-growing algorithm implemented in OpenMP

Three-dimensional memory of nuclear organization through cell cycles

The genome in the cell nucleus is organized by a dynamic process influenced by structural memory from mitosis. In this study, we develop a model of human genome dynamics through cell cycles by extending the previously developed whole-genome model to cover the mitotic phase. With this extension, we focus on the role of mitotic and cell cycle memory in genome organization. The simulation progresses from mitosis to interphase and the subsequent mitosis, leading to successive cell cycles. During mitosis, our model describes microtubule dynamics, showing how forces orchestrate the assembly of chromosomes into a rosette ring structure at metaphase. The model explains how the positioning of chromosomes depends on their size in metaphase. The memory of the metaphase configuration persists through mitosis and into interphase in dimensions perpendicular to the cell division axis, effectively guiding the distribution of chromosome territories over multiple cell cycles. At the onset of each G1 phase, phase separation of active and inactive chromatin domains occurs, leading to A/B compartmentalization. Our cycling simulations show that the compartments are unaffected by structural memory from previous cycles and are consistently established in each cell cycle. The genome model developed in this study highlights the interplay between chromosome dynamics and structural memory across cell cycles, providing insights for the analyses of cellular processes.

Saturation genome editing-based clinical classification of BRCA2 variants

Deep learning based screening model for hip diseases on plain radiographs

Introduction The interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs. Methods Electronic medical records and plain radiograph from 2004 to 2012 were used to construct two patient groups: the hip disease group (those who underwent total hip arthroplasty) and normal group. A total of 1,726 radiographs (500 normal hip radiographs and 1,226 radiographs with hip diseases, respectively) were included and were allocated for training (320 and 783), validation (80 and 196), and test (100 and 247) groups. Four different models were designed–raw image for both training and test set, preprocessed image for training but raw image for the test set, preprocessed images for both sets, and change of backbone algorithm from DenseNet to EfficientNet. The deep learning models were compared in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the receiver operating characteristic curve (AUROC). Results The mean age of the patients was 54.0 ± 14.8 years in the hip disease group and 49.8 ± 14.9 years in the normal group. The final model showed highest performance in both the internal test set (accuracy 0.96, sensitivity 0.96, specificity 0.97, PPV 0.99, NPV 0.99, F1-score 0.97, and AUROC 0.99) and the external validation set (accuracy 0.94, sensitivity 0.93, specificity 0.96, PPV 0.95, NPV 0.93, F1-score 0.94, and AUROC 0.98). In the gradcam image, while the first model depended on unrelated marks of radiograph, the second and third model mainly focused on the femur shaft and sciatic notch, respectively. Conclusion The deep learning-based model showed high accuracy and reliability in screening hip diseases on plain radiographs, potentially aiding physicians in more accurately diagnosing hip conditions.

Timing the Taming of Vascular Inflammation

Human responses to the DNA prime/chimpanzee adenovirus (ChAd63) boost vaccine identify CSP, AMA1 and TRAP MHC Class I-restricted epitopes

Background A three-antigen DNA-prime/chimpanzee adenovirus 63 (ChAd63) boost vaccine containing pre-erythrocytic Plasmodium falciparum (Pf) circumsporozoite protein (CSP), Pf apical membrane antigen-1 (AMA1) and malaria multiple epitopes (ME) fused to Pf thrombospondin-related adhesion protein (ME-TRAP) elicited higher vaccine efficacy (VE) in an open label, randomized Phase 1 trial against controlled human malaria infection (CHMI) than the two-antigen vaccine DNA/Human Adenovirus 5 (HuAd5) containing CSP and AMA1. The objective of this follow-up study was to determine whether responses to CSP, AMA1 or TRAP MHC Class I-restricted epitopes were associated with VE. Methodology Protected (n = 6) and non-protected participants (n = 26) were screened in FluoroSpot interferon gamma (IFN-γ) and Granzyme B (GzB) assays using antigen-specific 15mer peptide subpools spanning CSP (n = 9 subpools), AMA1 (n = 12 subpools), and TRAP (n = 11 subpools). Individual antigen-specific 15mers in the subpools with strong responses were then deconvoluted, evaluated for activities, and MHC Class I-restricted epitopes within the active 15mers were predicted using NetMHCpan algorithms. The predicted epitopes were synthesized and evaluated in the FluoroSpot IFN-γ and GzB assays. Results Protected and some non-protected participants had similar responses to individual antigen-specific peptide subpools, which did not distinguish only protected participants. However, deconvoluted antigen-specific positive subpools with high magnitudes of responses revealed individual 15mer peptides containing specific and/or predicted MHC Class I (HLA) epitopes. Responses to epitopes were either IFN-γ-only, IFN-γ and GzB, or GzB-only. Due to limitation of cells, most of the analysis concentrated on the identification of protection associated AMA1 epitopes, since most of the predominant pool specific responses were generated against AMA1 15mer subpools. Furthermore, we previously identified protection associated HLA class I-restricted epitopes in a previous gene-based vaccine trial. Seven predicted minimal epitopes in AMA1 were synthesized and upon testing, five recalled responses from protected participants confirming their possible contribution and association with protection, and two recalled responses from non-protected participants. Two protection-associated epitopes were promiscuous and may have also contributed to protection by recognition of different HLA alleles. In addition, strongly positive antigen-specific 15mers identified within active antigen-specific subpools contained 39 predicted but not tested epitopes were identified in CSP, AMA1 and TRAP. Finally, some non-protected individuals recognized HLA-matched protection-associated minimal epitopes and we discuss possible reasons. Other factors such as HLA allele fine specificity or interaction between other HLA alleles in same individual may also influence protective efficacy. Conclusions This integrated approach using immunoassays and bioinformatics identified and confirmed AMA1-MHC Class I-restricted epitopes and a list of predicted additional epitopes which could be evaluated in future studies to assess possible association with protection against CHMI in the Phase 1 trial participants. The results suggest that identification of protection-associated epitopes within malaria antigens is feasible and can help design potent next generation multi-antigen, multi-epitope malaria vaccines for a genetically diverse population and to develop robust assays to measure protective cellular immunity against pre-erythrocytic stages of malaria. This approach can be used to develop vaccines for other novel emerging infectious disease pathogens.