Non-invasive imaging of mechanical properties of cancers in vivo based on transformations of the Eshelby's tensor using compression elastography

Knowledge of the mechanical properties is of great clinical significance for diagnosis, prognosis and treatment of cancers. Recently, a new method based on Eshelby's theory to simultaneously assess Young's modulus (YM) and Poisson's ratio (PR) in tissues has been proposed. A significant limitation of this method is that accuracy of the reconstructed YM and PR is affected by the orientation/alignment of the tumor with the applied stress. In this paper, we propose a new method to reconstruct YM and PR in cancers that is invariant to the 3D orientation of the tumor with respect to the axis of applied stress. The novelty of the proposed method resides on the use of a tensor transformation to improve the robustness of Eshelby's theory and reconstruct YM and PR of tumors with high accuracy and in realistic experimental conditions. The method is validated using finite element simulations and controlled experiments using phantoms with known mechanical properties. The in vivo feasibility of the developed method is demonstrated in an orthotopic mouse model of breast cancer. Our results show that the proposed technique can estimate the YM and PR with overall accuracy of (97.06 ± 2.42) % under all tested tumor orientations. Animal experimental data demonstrate the potential of the proposed methodology in vivo. The proposed method can significantly expand the range of applicability of the Eshelby's theory to tumors and provide new means to accurately image and quantify mechanical parameters of cancers in clinical conditions.

Patterns and determinants of healthcare utilization and medication use before and during the COVID-19 crisis in Afghanistan, Bangladesh, and India

BACKGROUND

COVID-19 rapidly spread through South Asian countries and overwhelmed the health systems that were unprepared for such an outbreak. Evidence from high-income countries showed that COVID-19 impacted healthcare utilization, including medication use, but empirical evidence is lacking in South Asia. This study aimed to investigate the effect of COVID-19 on healthcare utilization and medication use in South Asia.

METHOD

The current study used longitudinal data from the 'Premise Health Service Disruption Survey' 2020 and 2021. The countries of interest were limited to Afghanistan, Bangladesh, and India. In these surveys, data related to healthcare utilization and medication use were collected for three-time points; 'Pre-COVID phase', 'Initial phase of COVID-19 outbreak', and 'One year of COVID-19 outbreak'. Generalized estimating equation (GEE) along with McNemar's test, Kruskal-Wallis test and χ2 test were applied in this study following the conceptualization of Andersen's healthcare utilization model.

RESULT

The use of healthcare and medication was unevenly impacted by the COVID-19 epidemic in Afghanistan, Bangladesh, and India. Immediately after the COVID-19 outbreak, respondents in Bangladesh reported around four times higher incomplete healthcare utilization compared to pre-COVID phase. In contrast, respondents in Afghanistan reported lower incomplete utilization of healthcare in a similar context. In the post COVID-19 outbreak, non-adherence to medication use was significantly higher in Afghanistan (OR:1.7; 95%CI:1.6,1.9) and India (OR:1.3; 95%CI:1.1,1.7) compared to pre-COVID phase. Respondents of all three countries who sought assistance to manage non-communicable diseases (NCDs) had higher odds (Afghanistan: OR:1.5; 95%CI:1.3,1.8; Bangladesh: OR: 3.7; 95%CI:1.9,7.3; India: OR: 2.3; 95% CI: 1.4,3.6) of non-adherence to medication use after the COVID-19 outbreak compared to pre-COVID phase.

CONCLUSION

The present study documented important evidence of the influence of COVID-19 epidemic on healthcare utilization and medication use in three countries of South Asia. Lessons learned from this study can feed into policy responses to the crisis and preparedness for future pandemics.

FAOT-Net: A 1.5-Stage Framework for 3D Pelvic Lymph Node Detection With Online Candidate Tuning

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.

Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI

AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.

Bladder Cancer and Artificial Intelligence: Emerging Applications

Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

Revealing hidden patterns in deep neural network feature space continuum via manifold learning

Deep neural networks (DNNs) extract thousands to millions of task-specific features during model training for inference and decision-making. While visualizing these features is critical for comprehending the learning process and improving the performance of the DNNs, existing visualization techniques work only for classification tasks. For regressions, the feature points lie on a high dimensional continuum having an inherently complex shape, making a meaningful visualization of the features intractable. Given that the majority of deep learning applications are regression-oriented, developing a conceptual framework and computational method to reliably visualize the regression features is of great significance. Here, we introduce a manifold discovery and analysis (MDA) method for DNN feature visualization, which involves learning the manifold topology associated with the output and target labels of a DNN. MDA leverages the acquired topological information to preserve the local geometry of the feature space manifold and provides insightful visualizations of the DNN features, highlighting the appropriateness, generalizability, and adversarial robustness of a DNN. The performance and advantages of the MDA approach compared to the existing methods are demonstrated in different deep learning applications.

Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors

The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.

Prevalence, pattern, and correlates of dyslipidemia in Bangladeshi individuals

BACKGROUND

The burden of dyslipidemia in Bangladesh remains inadequately characterized.

OBJECTIVES

To determine and describe the prevalence and pattern of dyslipidemia and its associated risk factors among an adult Bangladeshi population.

DESIGN

Population-based, cross-sectional study. Participants were adults living in all eight administrative divisions of Bangladesh. The total sample size was 7084 (53.1 % women, 46.9% urban residents). Primary outcome measures were triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the use of lipid lowering medication. In addition, control of LDL-C and control of non high-density lipoprotein cholesterol (non-HDL-C) were investigated.

RESULTS

The overall dyslipidemia prevalence was 76.7%, with 35.7% showing a high TG level, 18.5% showing a high LDL-C level, 63.8% showing a low HDL-C level, and 7.2% of the participants showing all three lipid abnormalities. Sylhet division had the highest prevalence (83.8%) of overall dyslipidemia, while Rangpur had the lowest prevalence (69.3%). The control of LDL-C (<50 mg/dL) and non-HDL-C (<80 mg/dL) among adults with a previous history of atherosclerotic cardiovascular diseases (ASCVD) were 5.1% and 6.9% respectively. The regression models showed that male sex and age 45-59 years were significant predictors of overall dyslipidemia. Both smokers and smokeless tobacco users were significant factors for overall dyslipidemia and high TG. A high waist-hip ratio was associated with overall dyslipidemia and all other subtypes of dyslipidemia.

CONCLUSION

The high prevalence of dyslipidemia in Bangladesh necessitates lifestyle interventions to prevent and control this cardiovascular risk factor.

Super-resolution biomedical imaging via reference-free statistical implicit neural representation

Objective.Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit neural representation (INR) framework, which needs only a single or a few observed low-resolution (LR) image(s), to generate high-quality SR images.Approach.The framework models the statistics of the observed LR images via maximum likelihood estimation and trains the INR network to represent the latent high-resolution (HR) image as a continuous function in the spatial domain. The INR network is constructed as a coordinate-based multi-layer perceptron, whose inputs are image spatial coordinates and outputs are corresponding pixel intensities. The trained INR not only constrains functional smoothness but also allows an arbitrary scale in SR imaging.Main results.We demonstrate the efficacy of the proposed framework on various biomedical images, including computed tomography (CT), magnetic resonance imaging (MRI), fluorescence microscopy, and ultrasound images, across different SR magnification scales of 2×, 4×, and 8×. A limited number of LR images were used for each of the SR imaging tasks to show the potential of the proposed statistical INR framework.Significance.The proposed method provides an urgently needed unsupervised deep learning framework for numerous biomedical SR applications that lack HR reference images.

Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data

Single-cell trajectory mapping and spatial reconstruction are two important developments in life science and provide a unique means to decode heterogeneous tissue formation, cellular dynamics, and tissue developmental processes. The success of these techniques depends critically on the performance of analytical tools used for high-dimensional (HD) gene expression data processing. Existing methods discern the patterns of the data without explicitly considering the underlying biological characteristics of the system, often leading to suboptimal solutions. Here, we present a cell-cell similarity-driven framework of genomic data analysis for high-fidelity spatial and temporal cellular mappings. The approach exploits the similarity features of the cells to discover discriminative patterns of the data. We show that for a wide variety of datasets, the proposed approach drastically improves the accuracies of spatial and temporal mapping analyses compared with state-of-the-art techniques.

A Self-Attention-Based Neural Network for Predicting Immune Checkpoint Inhibitors Response

PURPOSE/OBJECTIVE(S)

Cancer cells evade immune system by negatively regulating T cells via immune checkpoints (e.g., PD-1). By blocking these checkpoints, the ability of immune system to recognize and kill cancer cells restores. Individual response rate of checkpoint blockade varies among patients, with 50%-80% in specific types of cancer such as melanoma, while only 15%-30% in most other tumors. Yet it is still an open question what is the set of biomarkers that are crucial to the response to immune checkpoint inhibitors (ICI). The overall goal of this study is to develop and validate a biologically-aware interpretable deep learning model to identify the biomarkers that can predict the survival outcome to ICI treatment.

MATERIALS/METHODS

The self-attention mechanism could yield interpretable results where important biomarkers may have more "attention". However, in classical self-attention mechanism, the prior biological knowledge of protein interactions (PPI) and gene pathways are not incorporated. In this study, we propose a weighted biologically-aware attention score, where it is weighted against the gene centrality and pathway length. The genes that are closely connected to mutated genes receive 'high attention', while the genes that are far away from mutated genes along the pathway receive "lower attention". We then train, validate and test our model using 1,660 patients of nine types of cancer. To validate the prediction, 1. We evaluate the accuracy via concordance index. 2. We identified the genes that receive high attention and verify their functions in existed literature. 3. We perform sanity check by removing these genes from the data, re-training and predicting again, and comparing the prediction accuracy.

RESULTS

Our framework has achieved an average accuracy (measured via c-index) of 0.60 ± 0.06 for NSCLC and 0.58 ± 0.07 for melanoma, which is superior to both the gold standard COX-PH model (0.57 ± 0.06 for NSCLC and 0.53 ± 0.03 for melanoma) and DeepSurv (0.54 ± 0.05 for NSCLC and 0.51 ± 0.10 for melanoma). Genes that receive high attention have been validated by supporting literature, which provides an additional means of verifying the prediction in comparison to "black box" deep learning models, where there is no way to comprehend the reason behind predictions. Removing the top 8% high-attention genes (∼25 genes) from the data while using the remaining 92% for making predictions resulted in a drop in accuracy to 0.55 ± 0.073 for NSCLC and 0.56 ± 0.03 for melanoma, underscoring the significance of these genes. Patient stratification is also performed by dividing patients into responders and non-responders based on prediction score.

CONCLUSION

In this study, we propose and validate a biologically-aware self-attention based deep learning model which outperforms commonly-used survival models. Additionally, this tool has the potential to identify key biomarkers while assist in clinical decision-making, which demonstrates a promising step for immunotherapy response prediction.

Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

PURPOSE/OBJECTIVE(S)

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way.

MATERIALS/METHODS

We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM.

RESULTS

Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method.

CONCLUSION

The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

Cascade of diabetes care in Bangladesh, Bhutan and Nepal: identifying gaps in the screening, diagnosis, treatment and control continuum

Diabetes has become a major cause of morbidity and mortality in South Asia. Using the data from the three STEPwise approach to Surveillance (STEPS) surveys conducted in Bangladesh, Bhutan, and Nepal during 2018-2019, this study tried to quantify the gaps in diabetes screening, awareness, treatment, and control in these three South Asian countries. Diabetes care cascade was constructed by decomposing the population with diabetes (diabetes prevalence) in each country into five mutually exclusive and exhaustive categories: (1) unscreened and undiagnosed, (2) screened but undiagnosed, (3) diagnosed but untreated, (4) treated but uncontrolled, (5) treated and controlled. In Bangladesh, Bhutan, and Nepal, among the participants with diabetes, 14.7%, 35.7%, and 4.9% of the participants were treated and controlled, suggesting that 85.3%, 64.3%, and 95.1% of the diabetic population had unmet need for care, respectively. Multivariable logistic regression models were used to explore factors associated with awareness of the diabetes diagnosis. Common influencing factors for awareness of the diabetes diagnosis for Bangladesh and Nepal were living in urban areas [Bangladesh-adjusted odd ratio (AOR):2.1; confidence interval (CI):1.2, 3.6, Nepal-AOR:6.2; CI:1.9, 19.9].

Assessment of compression-induced solid stress, fluid pressure and mechanopathological parameters in cancers in vivo using poroelastography

OBJECTIVE

Compression-induced solid stress (SSc) and fluid pressure (FPc) during ultrasound poroelastography (USPE) experiments are correlated with two markers of cancer growth and treatment effectiveness: growth-induced solid stress (SSg) and interstitial fluid pressure (IFP). The spatio-temporal distributions of SSg and IFP are determined by the transport properties of the vessels and interstitium in the tumor microenvironment. &#xD;Approach. We propose a new USPE method for the non-invasive imaging of the local cancer mechanical parameters and dynamics of fluid flow. When performing poroelastography experiments, it may be difficult to implement a typical creep compression protocol, which requires to maintain a constant normally applied force. In this paper, we investigate the use of a stress relaxation protocol, which might be a more convenient choice for clinical poroelastography applications. &#xD;Main results. Based on our finite element (FE) and ultrasound (US) simulations study, we demonstrate that the SSc, FPc and their spatio-temporal distribution related parameters, interstitial permeability (IP) and vascular permeability (VP), can be determined from stress relaxation experiments with errors below 10% as compared to the ground truth and accuracy similar to that of corresponding creep tests, respectively. We also demonstrate the feasibility of the new methodology for in vivo experiments using a small animal cancer model.

SIGNIFICANCE

The proposed non-invasive USPE imaging methods may become an effective tool to assess local tumor pressure and mechanopathological parameters in cancers.

Learning image representations for content-based image retrieval of radiotherapy treatment plans

Objective.In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context.Approach.Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github.Main results.The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network.Significance.Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.

Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo

In this paper, new and non-invasive imaging methods to assess interstitial fluid transport parameters in tumors in vivo are developed, analyzed and experimentally validated. These parameters include extracellular volume fraction (EVF), interstitial fluid volume fraction (IFVF) and interstitial hydraulic conductivity (IHC), and they are known to have a critical role in cancer progression and drug delivery effectiveness. EVF is defined as the volume of extracellular matrix per unit volume of the tumor, while IFVF refers to the volume of interstitial fluid per unit bulk volume of the tumor. There are currently no established imaging methods to assess interstitial fluid transport parameters in cancers in vivo. We develop and test new theoretical models and imaging techniques to assess fluid transport parameters in cancers using non-invasive ultrasound methods. EVF is estimated via the composite/mixture theory with the tumor being modeled as a biphasic (cellular phase and extracellular phase) composite material. IFVF is estimated by modeling the tumor as a biphasic poroelastic material with fully saturated solid phase. Finally, IHC is estimated from IFVF using the well-known Kozeny-Carman method inspired by soil mechanics theory. The proposed methods are tested using both controlled experiments and in vivo experiments on cancers. The controlled experiments were performed on tissue mimic polyacrylamide samples and validated using scanning electron microscopy (SEM). In vivo applicability of the proposed methods was demonstrated using a breast cancer model implanted in mice. Based on the controlled experimental validation, the proposed methods can estimate interstitial fluid transport parameters with an error below 10% with respect to benchmark SEM data. In vivo results demonstrate that EVF, IFVF and IHC increase in untreated tumors whereas these parameters are observed to decrease over time in treated tumors. The proposed non-invasive imaging methods may provide new and cost-effective diagnostic and prognostic tools to assess clinically relevant fluid transport parameters in cancers in vivo.

Small-Object Sensitive Segmentation Using Across Feature Map Attention

Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This article presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.

Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis

Deep learning-based diagnosis is becoming an indispensable part of modern healthcare. For high-performance diagnosis, the optimal design of deep neural networks (DNNs) is a prerequisite. Despite its success in image analysis, existing supervised DNNs based on convolutional layers often suffer from their rudimentary feature exploration ability caused by the limited receptive field and biased feature extraction of conventional convolutional neural networks (CNNs), which compromises the network performance. Here, we propose a novel feature exploration network named manifold embedded multilayer perceptron (MLP) mixer (ME-Mixer), which utilizes both supervised and unsupervised features for disease diagnosis. In the proposed approach, a manifold embedding network is employed to extract class-discriminative features; then, two MLP-Mixer-based feature projectors are adopted to encode the extracted features with the global reception field. Our ME-Mixer network is quite general and can be added as a plugin to any existing CNN. Comprehensive evaluations on two medical datasets are performed. The results demonstrate that their approach greatly enhances the classification accuracy in comparison with different configurations of DNNs with acceptable computational complexity.

Implicit neural representation for radiation therapy dose distribution

Objective. Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 106 to 108. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data. Approach. Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function. Main results. Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10−6 and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ∼1 across all the datasets, with a compression ratio of ∼32. Our results also show that model sizes with a bitrate of 1–2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly. Significance. The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

Evaluation of Short Term Outcome of Stapled Transanal Rectal Resection (STARR) for ODS (Obstructed Defecation Syndrome) by Comparing Pre and Post-operative ODS Score

Obstructed defecation syndrome (ODS) is a common anorectal problem and it can be corrected by various surgical approaches but most of these have high recurrence and complication rates. Antonio Longo introduced Stapled transanal rectal resection (STARR) in 2003 as a minimally invasive transanal operation for correction ODS associated with rectocele and or rectal intussusception. This study was designed to assess the short term outcome of Stapled Transanal Rectal Resection (STARR) as a surgical treatment of Obstructed Defecation Syndrome (ODS). This is a quasi experimental study which was carried out in the department of Colorectal Surgery, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from May 2016 to June 2017. Seventeen (17) patients were included in the study. Patients with obstructed defecation syndrome and rectocele and or rectal intussusception admitted in the department of Colorectal Surgery were enrolled in the study as per inclusion and exclusion criteria. History, clinical examination, Proctoscopy, Colonoscopy and MR Defecography was done for evaluation of the patients. During evaluation preoperative Longo's ODS score of every patient also determined and compared with postoperative ODS score. The patient was followed up regularly at one, three and six months after each operation. The ODS score in 82.35% patients improved significantly. The postoperative score was high (13-15) only in 02(11.8%) patients probably due to presence of physiological factors. Post-operative defecatory urgency was developed in only 02(11.76%) patients. Major postoperative complication like hemorrhage or rectovaginal fistula did not develop in any patient. STARR is an effective, less invasive and simple procedure for the treatment of ODS with rectocele and/or rectal intussusception without major morbidity but other physiological causes of ODS should exclude preoperatively because its presence makes the surgical intervention fruitless.

Estimation of Mechanical and Transport Parameters in Cancers Using Short Time Poroelastography

Mechanical and transport properties of cancers such as Young's modulus (YM), Poisson's ratio (PR), and vascular permeability (VP) have great clinical importance in cancer diagnosis, prognosis, and treatment. However, non-invasive estimation of these parameters in vivo is challenged by many practical factors. Elasticity imaging methods, such as "poroelastography", require prolonged data acquisition, which can limit their clinical applicability. In this paper, we investigate a new method to perform poroelastography experiments, which results in shorter temporal acquisition windows. This method is referred to as "short-time poroelastography" (STPE). Finite element (FE) and ultrasound simulations demonstrate that, using STPE, it is possible to accurately estimate YM, PR (within 10% error) using windows of observation (WoOs) of length as short as 1 underlying strain Time Constant (TC). The error was found to be almost negligible (< 3%) when using WoOs longer than 2 strain TCs. In the case of VP estimation, WoOs of at least 2 strain TCs are required to obtain an error < 8% (in simulations). The stricter requirement for the estimation of VP with respect to YM and PR is due its reliance on the transient strain behavior while YM and PR depend on the steady state strain values only. In vivo experimental data are used as a proof-of-principle of the potential applicability of the proposed methodology in vivo. The use of STPE may provide a means to efficiently perform poroelastography experiments without compromising the accuracy of the estimated tissue properties.

Antimicrobial Resistance of E Coli Causing Urinary Tract Infection in Bangladesh

Rapid spread of multidrug resistant microorganisms is a matter of great concern throughout the glove including Bangladesh. The objective was to identify the causative organisms for urinary tract infection (UTI) and their sensitivity patterns to antibiotics. This descriptive cross-sectional study was conducted on patients admitted with UTI (n=60) at a tertiary level hospital in Dhaka, Bangladesh from March 2019 to September 2019. Data were collected through clinical record reviews. Data of all these 60 cases were analyzed for socio-demographic characteristics. Of the 60 patients, culture and sensitivity report was available for 42 patients. Therefore, data were further analyzed for these 42 cases. Median age of patients was 35 years and 80% were female. The main organisms isolated from urine culture of UTI patients were E. coli (64%), Klebsiella (12%) and Enterococci species (10%). Susceptibility to antibiotics was analyzed only for E. coli (n=27) since the number of isolates of other organisms were small. E. coli was found to be resistant to most of the first- and second-line antibiotics, such as Amoxicillin (100%), Amoxyclav (72%), Co-trimoxazole (89%), Nalidixic acid (78%), Ceftazidim (94%), Ceftriaxone (73%), Cefuroxime (100%), Ciprofloxacin (59%), Cephotaxime (80%), Cefixime (100%) and Moxifloxacin (100%). E. coli was the predominant organism responsible for UTI and was resistant to most of the first- and second-line antibiotics. Immediate action is needed to develop empirical guideline for empirical management of UTI and establish surveillance system for monitoring.

Artificial intelligence in image-guided radiotherapy: a review of treatment target localization

Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.

Milestones of Development in Infancy: A Prospective Study in a Tertiary Level Hospital of Bangladesh

The timing of milestone is influenced by many factors. Sex and socioeconomic status has significant effect to some psycomotor milestones. The study was conducted to determine the pattern of milestones of development of infants in our country and to compare it in different sex and socioeconomic condition. It was a hospital based prospective study done in Sir Sallimullah Medical College and Mitford Hospital, Dhaka, Bangladesh from October 2014 to November 2015. Healthy term newborn infants with average birth weight were included in this study and milestones of this birth cohort were assessed monthly from birth to 12 months of age by using a set of 60 milestones. Total number of 217 babies was enrolled but during follow up 0.9% developed meningitis, 43.7% was lost to follow up and 55.2% of the cohort was followed up to 12 months of age. Among 120 babies 51.7% were male, 48.3% were female babies and 51.7% belong to lower, 32.5% middle and 15.8% upper socioeconomic group. There was no significant difference between male and female infants achieving most of the milestones of development except in language development in which female infants were little bit higher than male infants.

Diagnostic Value of Clinical Profile and Proposed a Clinical Diagnostic Criterion of Enteric Fever

Enteric fever is a common bacterial infection in the tropics and endemic to Bangladesh. The volatile manifestations of enteric fever construct this disease a true diagnostic confrontation. There are limited current objective data on the value of individual clinical features of enteric fever in the diagnosis of enteric fever. The aim of the study was analysis of clinical features and also proposed a clinical diagnostic criterion of enteric fever among adult in Bangladesh. This cross-sectional comparative study was performed among which of fifty confirmed enteric fever and hundred non enteric febrile adult patients in Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh from January 2015 to December 2015. Purposive sampling technique was implied for convenience of the study. In this study, history of step ladder fever, diarrhoea and relative bradycardia, ceacal gurgle, abdominal distension were proved to be powerful markers of enteric fever with high specificity (100.0%, 90.0%, 95.0%, 92.0% and 95.0% respectively). Tender right iliac fossa (RIF) and coated tongue, hepatomegaly were moderately powerful with 86.0%, 88.0%, 89.0% specificity respectively. Positive predictive value (PPV) was highest for step ladder fever (100%) and negative predictive value (NPV) was highest for headache (92.5%). Highest sensitivity, PPV and NPV were found for relative bradycardia and tender RIF but most of the signs had good specificity. Regarding accuracy it was highest for step ladder fever (91.3%), relative bradycardia (94%), tender RIF (87%), coated tongue (82%) and splenomegaly (84%). Therefore, a clinical diagnostic criterion was submitted with diagnostic accuracy more than 70% were taken into deliberation. The Major criteria were considered step ladder fever, relative bradycardia, tender RIF with diagnostic accuracy 91.0%, 94.0% and 87.0% respectively. Minor criteria included splenomegaly, diarrhoea, coated tongue, ceacal gurgle, chills with diagnostic accuracy 85.0%, 85.0%, 82.0%, 76.0%, 72.0% respectively and after amalgamation of various major and minor criteria a final diagnostic criterion was submitted having accuracy more than 60.0%. In conclusion the clinical profile of enteric fever in culture proven patients with a view to highlight the predictive value of those features which would help general practitioners in the diagnosis and empiric treatment.

Influence of height on blood pressure and hypertension among Bangladeshi adults

Background

Recent studies have reported that height is inversely associated with blood pressure and hypertension. However, there is lack of comprehensive findings from Bangladesh in this regard.

Objective

The purpose of this study was to explore the association between height and blood pressure in a Bangladeshi population.

Setting

Rural and urban sites from seven divisions of Bangladesh.

Participants

Participants were 7932 males and females (aged ≥35 years) evaluated in the 2011 Bangladesh Demographic Health Survey. Participants (n = 7647) who had complete height, weight, systolic and diastolic blood pressure (SBP and DBP) measurements and non-missing medication history, were included in the analysis.

Methods

Hypertension was defined as an SBP over 140 mmHg or/and a DBP over 90 mmHg, or current use of antihypertensive medication. Difference between SBP and DBP was calculated to get pulse pressure (PP). Multivariate linear and logistic regression models were used.

Results

PP decreased linearly with increasing height among males (−0.11, P < 0.05) and females (−0.19, P < 0.05) after adjusting for age, BMI, living region, type of occupation, wealth index, and highest level of education. SBP decreased linearly with increasing height among only females (−0.14, P < 0.05), after adjusting for age, BMI, living region, type of occupation, wealth index, and highest level of education. No association was found between quartiles of height and prevalence of hypertension.

Conclusions

Height was found to be inversely associated with pulse pressure in both sexes. Studies with longitudinal design are needed to investigate the association between shortness with blood pressure and hypertension.

Prevalence, risk factors, and interventions for chronic obstructive pulmonary disease in South Asia: a scoping review protocol

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is increasingly contributing to the disease burden in South Asia. This review will summarize the prevalence and risk factors of COPD in South Asia and the interventions regarding COPD that have been introduced in South Asian countries.

METHOD

This scoping review will primarily follow Arksey and O'Malley's six steps of scoping review methodology. Additionally, it will follow the recent upgradation of the scoping review methodology by Levac et al., and the Joanna Briggs Institute. Research questions were already identified at the beginning of the proposed scoping review. Electronic databases will be searched (PubMed, Web of Science, and ProQuest) using search terms. Studies will be screened independently by two reviewers through a two-stage screening process using pre-developed inclusion criteria for this scoping review. Eligible studies will be abstracted and charted in a standardised form. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be used to report the result. Additionally, feedback from South Asia's experienced COPD researchers on the final literature list will be collected for gap identification in literature search. Two independent reviewers will assess the quality of each included study's design using the Joanna Briggs Institute's tool.

DISCUSSION

The proposed scoping review will map the evidence on COPD in South Asia through literature review, and it will focus on prevalence, risk factors, and interventions. This review will contribute to the advancement of research on COPD and will be beneficial for policy-makers, public health specialists, and clinicians.

COVID-19 Rehabilitation Response in Bangladesh: Ongoing Efforts and Future Directives

COVID-19 pandemic brings significant number of post-acute and chronic disabilities requiring attention to Physical Medicine and Rehabilitation (PMR) services. Total Health and Rehabilitation sector in Bangladesh is overwhelmed; patient care and academic activities are seriously impacted by this pandemic. Rehabilitation team works and academic calendar is disrupted. Bangladesh PMR working to manage COVID-19 imposed rehabilitation challenges with adjustment and adaptations of the existing facilities. There is an urgent need to undertake additional measures promptly, including rehabilitation capacity building anticipating the potential challenge that would be faced by the hospitals in the estimated upsurge of COVID-19 cases and its complications thereafter. This topic highlights the activity log for COVID-19 preparedness and mitigation for rehabilitation services in Bangladesh with a message for other rehabilitation settings in the world.

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline. Our code is available at GitHub.

Non-invasive imaging of Young's modulus and Poisson's ratio in cancers in vivo

Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often early indicators of the onset of pathological conditions. Knowledge of these parameters has been proven to be of great clinical significance for the diagnosis, prognosis and treatment of cancers. Currently, however, there are no non-invasive modalities that can be used to image and quantify these parameters in vivo without assuming incompressibility of the tissue, an assumption that is rarely justified in human tissues. In this paper, we developed a new method to simultaneously reconstruct YM and PR of a tumor and of its surrounding tissues based on the assumptions of axisymmetry and ellipsoidal-shape inclusion. This new, non-invasive method allows the generation of high spatial resolution YM and PR maps from axial and lateral strain data obtained via ultrasound elastography. The method was validated using finite element (FE) simulations and controlled experiments performed on phantoms with known mechanical properties. The clinical feasibility of the developed method was demonstrated in an orthotopic mouse model of breast cancer. Our results demonstrate that the proposed technique can estimate the YM and PR of spherical inclusions with accuracy higher than 99% and with accuracy higher than 90% in inclusions of different geometries and under various clinically relevant boundary conditions.

A Spline Interpolation-based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography

Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young's modulus, Poisson's ratio, interstitial permeability, and vascular permeability. To estimate interstitial permeability and vascular permeability using poroelastography, accurate estimation of the strain time constant (TC) is required. This can be a challenging task due to the nonlinearity of the exponential strain curve and noise affecting the experimental data. Due to motion artifacts caused by the sonographer, animal/patient, and/or the environment, noise affecting some strain frames can be significantly higher than the strain signal. If these frames are used for the computation of the strain TC, the resulting TC estimate can be highly inaccurate, which, in turn, can cause high errors in the reconstructed mechanical parameters. In this paper, we introduce a cubic spline-based interpolation method, which allows to use only good quality strain frames (i.e., frames with sufficiently high signal-to-noise ratio [SNR]) to estimate the strain TC. Using finite element simulations, we demonstrate that the proposed interpolation method can improve the estimation accuracy of the strain TC by 46% with respect to the case where no interpolation and filtering are used and by 37% with respect to the case where the strain frames are Kalman filtered before TC estimation (at an SNR of 30 dB). We also prove the technical feasibility of the proposed technique using in vivo experimental data. While detecting the bad frames in both simulations and experiments, we assumed the lower limit SNR to be below 10 dB. Based on our results, the proposed technique may be of great help in applications relying on the accurate assessment of the temporal behavior of strain data.

Legg-Calve-Perthes Disease: Correlation between Computed Radiography and Magnetic Resonance Imaging

The purpose of this study is to diagnose Legg-Calve-Perthes disease by computed radiography and Magnetic resonance imaging and accurate staging and correlating the findings of these two modalities. Thirty five (35) patients complaining pain in groins and painful walking, after thorough physical examinations were sent to Department of Radiology & Imaging, Mymensingh Medical College Hospital, Mymensingh, Bangladesh for computed radiography and Magnetic Resonance Imaging examination. This cross sectional study was conducted in the Department of Radiology, Mymensingh Medical College Hospital, Mymensingh, Bangladesh from July 2016 to June 2018. Legg-Calve-Perthes disease among the selected 35 cases were started at the age of 5(2.9%) and age range of the patients were 5-13 years; mean age was (9.63±1.82) years and most of them belonged to 8-10 years of age (51.4%). Patients with Legg-Calve-Perthes disease had been suffering from pain in right and left groins forvarious durations. Maximum duration was 1-2 years (~88.57%). Maximum proportion of diagnosed patients was delivered by normal delivery (60%) and maximum proportion of patients was premature (65.7%). Most of the patients were low birth weight baby (65.7%). Here chi-square test was done and found no significant relationship between delivery mode and birth weight in case of Legg-Calve-Perthesdisease (x²=1.712) (P=0.191). The result of the X-ray and MRI findings by cross table of chi square test found fair inter relationship between two diagnostic instruments. Result found fine difference in staging of the disease between X-ray and MRI findings. It can be said that MRI definitely a better tool for early diagnosis of Legg-Calve-Perthes disease and its staging but X-ray modality can be used. A primary tool for diagnosis and staging of the disease can be done where the MRI facility is not available or cost expensive for patient.

Thermal gradient driven domain wall dynamics

The issue of whether a thermal gradient acts like a magnetic field or an electric current in the domain wall (DW) dynamics is investigated. Broadly speaking, magnetization control knobs can be classified as energy-driving or angular-momentum driving forces. DW propagation driven by a static magnetic field is the best known example of the former in which the DW speed is proportional to the energy dissipation rate, and the current-driven DW motion is an example of the latter. Here we show that DW propagation speed driven by a thermal gradient can be fully explained as the angular momentum transfer between thermally generated spin current and DW. We found DW-plane rotation speed increases as DW width decreases. Both DW propagation speed along the wire and DW-plane rotation speed around the wire decrease with the Gilbert damping. These facts are consistent with the angular momentum transfer mechanism, but are distinct from the energy dissipation mechanism. We further show that magnonic spin-transfer torque (STT) generated by a thermal gradient has both damping-like and field-like components. By analyzing DW propagation speed and DW-plane rotational speed, the coefficient ([Formula: see text]) of the field-like STT arising from the non-adiabatic process, is obtained. It is found that [Formula: see text] does not depend on the thermal gradient; increases with uniaxial anisotropy [Formula: see text] (thinner DW); and decreases with the damping, in agreement with the physical picture that a larger damping or a thicker DW leads to a better alignment between the spin-current polarization and the local magnetization, or a better adiabaticity.

A Low Cost and Portable Microwave Imaging System for Breast Tumor Detection Using UWB Directional Antenna array

Globally, breast cancer is a major reason for female mortality. Due to the limitations of current clinical imaging, the researchers are encouraged to explore alternative and complementary tools to available techniques to detect the breast tumor in an earlier stage. This article outlines a new, portable, and low-cost microwave imaging (MWI) system using an iterative enhancing technique for breast imaging. A compact side slotted tapered slot antenna is designed for microwave imaging. The radiating fins of tapered slot antenna are modified by etching nine rectangular side slots. The irregular slots on the radiating fins enhance the electrical length as well as produce strong directive radiation due to the suppression of induced surface currents that radiate vertically at the outer edges of the radiating arms with end-fire direction. It has remarkable effects on efficiency and gain. With the addition of slots, the side-lobe levels are reduced, the gain of the main-lobe is increased and corrects the squint effects simultaneously, thus improving the characteristics of the radiation. For experimental validation, a heterogeneous breast phantom was developed that contains dielectric properties identical to real breast tissues with the inclusion of tumors. An alternative PC controlled and microcontroller-based mechanical MWI system is designed and developed to collect the antenna scattering signal. The radiated backscattered signals from the targeted area of the human body are analyzed to reveal the changes in dielectric properties in tissues. The dielectric constants of tumorous cells are higher than that of normal tissues due to their higher water content. The remarkable deviation of the scattered field is processed by using newly proposed Iteratively Corrected Delay and Sum (IC-DAS) algorithm and the reconstruction of the image of the phantom interior is done. The developed UWB (Ultra-Wideband) antenna based MWI has been able to perform the detection of tumorous cells in breast phantom that can pave the way to saving lives.

Non-Invasive Imaging of Normalized Solid Stress in Cancers in Vivo

The solid stress (SSg) that develops inside a cancer is an important marker of cancer’s growth, invasion and metastasis. Currently, there are no non-invasive methods to image SSg inside tumors. In this paper, we develop a new, non-invasive and cost-effective imaging method to assess SSg inside tumors that uses ultrasound poroelastography. Center to the proposed method is a novel analytical model, which demonstrates that SSg and the compression-induced stress (SSc) that generates inside the cancer in a poroelastography experiment have the same spatial distribution. To show the clinical feasibility of the proposed technique, we imaged and analyzed the normalized SSg inside treated and untreated human breast cancers in a small animal model. Given the clinical significance of assessing SSg in cancers and the advantages of the proposed ultrasonic methods, our technique could have a great impact on cancer diagnosis, prognosis and treatment methods.

Prevalence and correlates of cardiometabolic multimorbidity among hypertensive individuals: a cross-sectional study in rural South Asia-Bangladesh, Pakistan and Sri Lanka

OBJECTIVE

To determinate the prevalence and correlates of cardiometabolic multimorbidity (CMM), and their cross-country variation among individuals with hypertension residing in rural communities in South Asia.

DESIGN

A cross-sectional study.

SETTING

Rural communities in Bangladesh, Pakistan and Sri Lanka.

PARTICIPANTS

A total of 2288 individuals with hypertension aged ≥40 years from the ongoing Control of Blood Pressure and Risk Attenuation- Bangladesh, Pakistan and Sri Lanka clinical trial.

MAIN OUTCOME MEASURES

CMM was defined as the presence of ≥2 of the conditions: diabetes, chronic kidney disease, heart disease and stroke. Logistic regression was done to evaluate the correlates of CMM.

RESULTS

About 25.4% (95% CI 23.6% to 27.2%) of the hypertensive individuals had CMM. Factors positively associated with CMM included residing in Bangladesh (OR 3.42, 95% CI 2.52 to 4.65) or Sri Lankan (3.73, 95% CI 2.48 to 5.61) versus in Pakistan, advancing age (2.33, 95% CI 1.59 to 3.40 for 70 years and over vs 40-49 years), higher waist circumference (2.15, 95% CI 1.42 to 3.25) for Q2-Q3 and 2.14, 95% CI 1.50 to 3.06 for Q3 and above), statin use (2.43, 95% CI 1.84 to 3.22), and higher levels of triglyceride (1.01, 95% CI 1.01 to 1.02 per 5 mg/dL increase). A lower odds of CMM was associated with being physically active (0.75, 95% CI 0.57 to 0.97). A weak inverted J-shaped association between International Wealth Index and CMM was found (p for non-linear=0.058), suggesting higher risk in the middle than higher or lower socioeconomic strata.

CONCLUSIONS

CMM is highly prevalent in rural South Asians affecting one in four individuals with hypertension. There is an urgent need for strategies to concomitantly manage hypertension, cardiometabolic comorbid conditions and associated determinants in South Asia.

Estimation of Vascular Permeability in Irregularly Shaped Cancers Using Ultrasound Poroelastography

OBJECTIVE

Vascular permeability (VP) is a mechanical parameter which plays an important role in cancer initiation, metastasis, and progression. To date, there are only a few non-invasive methods that can be used to image VP in solid tumors. Most of these methods require the use of contrast agents and are expensive, limiting widespread use.

METHODS

In this paper, we propose a new method to image VP in tumors, which is based on the use of ultrasound poroelastography. Estimation of VP by poroelastography requires knowledge of the Young's modulus (YM), Poisson's ratio (PR), and strain time constant (TC) in the tumors. In our method, we find the ellipse which best fits the tumor (regardless of its shape) using an eigen-system-based fitting technique and estimate the YM and PR using Eshelby's elliptic inclusion formulation. A Fourier method is used to estimate the axial strain TC, which does not require any initial guess and is highly robust to noise.

RESULTS

It is demonstrated that the proposed method can estimate VP in irregularly shaped tumors with an accuracy of above [Formula: see text] using ultrasound simulation data with signal-to-noise ratio of 20 dB or higher. In vivo feasibility of the proposed technique is demonstrated in an orthotopic mouse model of breast cancer.

CONCLUSION

The proposed imaging method can provide accurate and localized estimation of VP in cancers non-invasively and cost-effectively.

SIGNIFICANCE

Accurate and non-invasive assessment of VP can have a significant impact on diagnosis, prognosis, and treatment of cancers.

Estimation of mechanical parameters in cancers by empirical orthogonal function analysis of poroelastography data

Ultrasound poroelastography is a non-invasive imaging modality that has been shown to be capable of estimating mechanical parameters such as Young's modulus (YM), Poisson's ratio (PR) and vascular permeability (VP) in cancers. However, experimental poroelastographic data are inherently noisy because of the requirement of relatively long temporal data acquisitions often in hand-held mode conditions. In this paper, we propose a new method, which allows accurate estimation of YM and PR from denoised steady state axial and lateral strains by empirical orthogonal function (EOF) analysis of poroelastographic data. The method also allows estimation of VP from the time constant (TC) of the first expansion coefficient (EC) of the temporal axial strain, which has larger dynamic range and lower noise in comparison to the actual temporal axial strain curve. We validated our technique through finite element (FE) and ultrasound simulations and tested the in vivo feasibility in experimental data obtained from a cancer animal model. The percent relative errors (PRE) in the estimation of YM, PR and VP using the EOF analysis as applied to ultrasound simulation data were 3.27%, 3.10%, 14.22%, respectively (at SNR of 20 dB). Based on the high level of accuracy by EOF analysis, the proposed technique may become a useful signal processing technique for applications focusing on the estimation of the mechanical behavior of cancers.

A Robust Method to Estimate the Time Constant of Elastographic Parameters

Novel viscoelastic and poroelastic elastography techniques rely on the accurate estimation of the temporal behavior of the axial or lateral strains and related parameters. From the temporal curve of the elastographic parameter of interest, the time constant (TC) is estimated using analytical models and curve-fitting techniques such as Levenberg-Marquardt (LM), Nelder-Mead (NM), and trust-region reflective (TR). In this paper, we propose a new technique named variable projection (VP) to estimate accurately and robustly the TC and steady-state value of the elastographic parameter of interest from its temporal curve. As a testing platform, the method is used with a novel analytical model, which can be used for both poroelastic and viscoelastic tissues and in most practical experimental conditions of clinical interest. Finite element and ultrasound simulations and experimental results demonstrate that VP is robust to noise and capable of estimating the TC of the elastographic parameter with accuracy higher than that of typically employed curve-fitting techniques. The results also demonstrate that the performance of VP is not affected by an incorrect initial TC guess. For example, in simulations, VP can estimate the TC of axial strain and effective Poisson's ratio accurately for initial guesses ranging from 0.001 to infinite times of the true TC value even in fairly noisy conditions (30-dB signal to noise ratio). In experiments, VP always estimates the axial strain TC reliably, whereas the LM, NM, and TR methods fail to converge or converge to wrong solutions in most of the cases.

Assessment of the long bone inter-fragmentary gap size in ultrasound strain elastograms

The inter-fragmentary gap size (IFGS) is a critical factor affecting the propensity of bone healing. In this paper, we present a study to analyze ultrasound strain elastographic numerical features in samples with distinct IFGS using both simulations and experiments. Six fractured rabbit hind leg samples in total were used in this study with controlled IFGS of 1 mm, 5 mm and 1 cm. For the simulation, computed tomography (CT) scans of all six samples were used to create solid models. Finite element analysis (FEA) and subsequent elastography simulations were performed on the 3D models to produce tensorial strain field data. Features of bony fragment separation were defined on different strain components and computed for strains segmented at varying thresholds to evaluate their performance in estimating the IFGS. A threshold for each strain component was then determined, based on which extra 3D features of interest were defined and extracted from the segmented strain data. Then, all 3D features were compared statistically among the three nominal groups. Additional simulations and experiments of axial shear strain elastography (ASSE) on the median coronal plane of the same samples were also performed. Our results indicate that coronal plane axial shear (CPAS) strain elastography produces a separation feature which is statistically correlated with the IFGS, and that our elastography simulation module is effective in predicting the CPAS elastographic strain behavior for different IFGS.

Regional Variation in Comorbid Prediabetes and Diabetes and Associated Factors among Hypertensive Individuals in Rural Bangladesh, Pakistan, and Sri Lanka

We aimed to explore the cross-country variation in the prevalence of comorbid prediabetes or diabetes and determine the sociodemographic, lifestyle, and clinical factors, especially body mass index (BMI) and waist circumference, associated with comorbid diabetes in individuals with hypertension in rural South Asia. We analyzed cross-sectional data of 2426 hypertensive individuals of ≥40 years from 30 randomly selected rural communities in Bangladesh, Pakistan, and Sri Lanka. Prediabetes was defined as fasting plasma glucose (FPG) between 100 and 125 mg/dL without use of antidiabetic treatment and diabetes as FPG ≥126 mg/dL or use of antidiabetic medication. The prevalence (95% CI) of prediabetes or diabetes (53.5% (51.5%, 55.5%)) and diabetes (27.7% (25.9%, 29.5%)) was high in the overall hypertensive study population in rural communities in 3 countries. Rural communities in Sri Lanka had the highest crude prevalence of prediabetes or diabetes and diabetes (73.1% and 39.3%) with hypertension, followed by those in Bangladesh (47.4% and 23.1%) and Pakistan (39.2% and 20.5%). The factors independently associated with comorbid diabetes and hypertension were residing in rural communities in Sri Lanka, higher education, international wealth index, waist circumference, pulse pressure, triglyceride, and lower high-density lipoprotein. The association of diabetes with waist circumference was stronger than with BMI in hypertensive individuals. Prediabetes or diabetes are alarmingly common among adults with hypertension and vary among countries in rural South Asia. The high prevalence of comorbid diabetes in Sri Lanka among hypertensives is not fully explained by conventional risk factors and needs further etiological research. Urgent public health efforts are needed to integrate diabetes control within hypertension management programs in rural South Asia, including screening waist circumference.

A Model-Based Approach to Investigate the Effect of a Long Bone Fracture on Ultrasound Strain Elastography

The mechanical behavior of long bones and fractures has been under investigation for many decades due to its complexity and clinical relevance. In this paper, we report a new subject-specific methodology to predict and analyze the mechanical behavior of the soft tissue at a bone interface with the intent of identifying the presence and location of bone abnormalities with high accuracy, spatial resolution, and contrast. The proposed methodology was tested on both intact and fractured rabbit femur samples with finite element-based 3-D simulations, created from actual femur computed tomography data, and ultrasound elastography experiments. The results included in this study demonstrate that elastographic strains at the bone/soft tissue interface can be used to differentiate fractured femurs from the intact ones on a distribution level. These results also demonstrate that coronal plane axial shear strain creates a unique contrast mechanism that can be used to reliably detect fractures (both complete and incomplete) in long bones. Kruskal-Wallis test further demonstrates that the contrast measure for the fracture group (simulation: 2.1286±0.2206; experiment: 2.7034 ± 1.0672) is significantly different from that for the intact group (simulation: 0 ± 0; experiment: 1.1540±0.6909) when using coronal plane axial shear strain elastography ( < 0.01). We conclude that: 1) elastography techniques can be used to accurately identify the presence and location of fractures in a long bone and 2) the proposed model-based approach can be used to predict and analyze strains at a bone fracture site and to better interpret experimental elastographic data.

A Model-Based Approach to Investigate the Effect of a Long Bone Fracture on Ultrasound Strain Elastography

The mechanical behavior of long bones and fractures has been under investigation for many decades due to its complexity and clinical relevance. In this paper, we report a new subject-specific methodology to predict and analyze the mechanical behavior of the soft tissue at a bone interface with the intent of identifying the presence and location of bone abnormalities with high accuracy, spatial resolution, and contrast. The proposed methodology was tested on both intact and fractured rabbit femur samples with finite element-based 3-D simulations, created from actual femur computed tomography data, and ultrasound elastography experiments. The results included in this study demonstrate that elastographic strains at the bone/soft tissue interface can be used to differentiate fractured femurs from the intact ones on a distribution level. These results also demonstrate that coronal plane axial shear strain creates a unique contrast mechanism that can be used to reliably detect fractures (both complete and incomplete) in long bones. Kruskal-Wallis test further demonstrates that the contrast measure for the fracture group (simulation: 2.1286±0.2206; experiment: 2.7034 ± 1.0672) is significantly different from that for the intact group (simulation: 0 ± 0; experiment: 1.1540±0.6909) when using coronal plane axial shear strain elastography ( < 0.01). We conclude that: 1) elastography techniques can be used to accurately identify the presence and location of fractures in a long bone and 2) the proposed model-based approach can be used to predict and analyze strains at a bone fracture site and to better interpret experimental elastographic data.