Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

Comparative Analysis of Viral Gene Expression Programs during Poxvirus Infection: A Transcriptional Map of the Vaccinia and Monkeypox Genomes

Poxviruses engage in a complex and intricate dialogue with host cells as part of their strategy for replication. However, relatively little molecular detail is available with which to understand the mechanisms behind this dialogue.We designed a specialized microarray that contains probes specific to all predicted ORFs in the Monkeypox Zaire (MPXV) and Vaccinia Western Reserve (VACV) genomes, as well as >18,000 human genes, and used this tool to characterize MPXV and VACV gene expression responses in vitro during the course of primary infection of human monocytes, primary human fibroblasts and HeLa cells. The two viral transcriptomes show distinct features of temporal regulation and species-specific gene expression, and provide an early foundation for understanding global gene expression responses during poxvirus infection.The results provide a temporal map of the transcriptome of each virus during infection, enabling us to compare viral gene expression across species, and classify expression patterns of previously uncharacterized ORFs

Transcriptomic Profiling of Virus-Host Cell Interactions following Chicken Anaemia Virus (CAV) Infection in an In Vivo Model.

Chicken Anaemia Virus (CAV) is an economically important virus that targets lymphoid and erythroblastoid progenitor cells leading to immunosuppression. This study aimed to investigate the interplay between viral infection and the host's immune response to better understand the pathways that lead to CAV-induced immunosuppression. To mimic vertical transmission of CAV in the absence of maternally-derived antibody, day-old chicks were infected and their responses measured at various time-points post-infection by qRT-PCR and gene expression microarrays. The kinetics of mRNA expression levels of signature cytokines of innate and adaptive immune responses were determined by qRT-PCR. The global gene expression profiles of mock-infected (control) and CAV-infected chickens at 14 dpi were also compared using a chicken immune-related 5K microarray. Although in the thymus there was evidence of induction of an innate immune response following CAV infection, this was limited in magnitude. There was little evidence of a Th1 adaptive immune response in any lymphoid tissue, as would normally be expected in response to viral infection. Most cytokines associated with Th1, Th2 or Treg subsets were down-regulated, except IL-2, IL-13, IL-10 and IFNγ, which were all up-regulated in thymus and bone marrow. From the microarray studies, genes that exhibited significant (greater than 1.5-fold, false discovery rate <0.05) changes in expression in thymus and bone marrow on CAV infection were mainly associated with T-cell receptor signalling, immune response, transcriptional regulation, intracellular signalling and regulation of apoptosis. Expression levels of a number of adaptor proteins, such as src-like adaptor protein (SLA), a negative regulator of T-cell receptor signalling and the transcription factor Special AT-rich Binding Protein 1 (SATB1), were significantly down-regulated by CAV infection, suggesting potential roles for these genes as regulators of viral infection or cell defence. These results extend our understanding of CAV-induced immunosuppression and suggest a global immune dysregulation following CAV infection

The effect of type of femoral component fixation on mortality and morbidity after hip hemiarthroplasty:A systematic review and meta-analysis

Background: Hip hemiarthroplasty is a well-established treatment of displaced femoral neck fracture, although debate exists over whether cemented or uncemented fixation is superior. Uncemented prostheses have typically been used in younger, healthier patients and cemented prostheses in older patients with less-stable bone. Also, earlier research has suggested that bone cement has cytotoxic effects and may trigger cardiovascular and respiratory adverse events. Questions/Purposes: The aim of this systematic review and meta-analysis was to compare morbidity and mortality rates after cemented and uncemented hemiarthroplasty for the treatment of displaced femoral neck fractures in elderly patients. Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched seven medical databases for randomized clinical trials and observational studies. We compared cemented and uncemented hemiarthroplasty using the Harris Hip Score (HHS), as well as measures of postoperative pain, mortality, and complications. Data were extracted and pooled as risk ratios or standardized mean difference with their corresponding 95% confidence intervals in a meta-analysis model. Results: The meta-analysis included 34 studies (12 randomized trials and 22 observational studies), with a total of 42,411 patients. In the pooled estimate, cemented hemiarthroplasty was associated with less risk of postoperative pain than uncemented hemiarthroplasty. There were no significant differences between groups regarding HHS or rates of postoperative mortality, pulmonary embolism, cardiac arrest, myocardial infarction, acute cardiac arrhythmia, or deep venous thrombosis. Conclusions: While we found that cemented hemiarthroplasty results in less postoperative pain than uncemented hemiarthroplasty in older patients with femoral neck fracture, the lack of significant differences in functional hip scores, mortality, and complications was surprising. Further high-level research is needed

Vertebral rotation measurement: a summary and comparison of common radiographic and CT methods

Current research has provided a more comprehensive understanding of Adolescent Idiopathic Scoliosis (AIS) as a three-dimensional spinal deformity, encompassing both lateral and rotational components. Apart from quantifying curve severity using the Cobb angle, vertebral rotation has become increasingly prominent in the study of scoliosis. It demonstrates significance in both preoperative and postoperative assessment, providing better appreciation of the impact of bracing or surgical interventions. In the past, the need for computer resources, digitizers and custom software limited studies of rotation to research performed after a patient left the scoliosis clinic. With advanced technology, however, rotation measurements are now more feasible. While numerous vertebral rotation measurement methods have been developed and tested, thorough comparisons of these are still relatively unexplored. This review discusses the advantages and disadvantages of six common measurement techniques based on technology most pertinent in clinical settings: radiography (Cobb, Nash-Moe, Perdriolle and Stokes' method) and computer tomography (CT) imaging (Aaro-Dahlborn and Ho's method). Better insight into the clinical suitability of rotation measurement methods currently available is presented, along with a discussion of critical concerns that should be addressed in future studies and development of new methods

Global mortality from dementia: Application of a newmethod and results from the global burden of disease study 2019

INTRODUCTION: Dementia is currently one of the leading causes of mortality globally, and mortality due to dementia will likely increase in the future along with corresponding increases in population growth and population aging. However, large inconsistencies in coding practices in vital registration systems over time and between countries complicate the estimation of global dementia mortality. METHODS: We meta-analyzed the excess risk of death in those with dementia and multiplied these estimates by the proportion of dementia deaths occurring in those with severe, end-stage disease to calculate the total number of deaths that could be attributed to dementia. RESULTS: We estimated that there were 1.62 million (95% uncertainty interval [UI]: 0.41–4.21) deaths globally due to dementia in 2019. More dementia deaths occurred in women (1.06 million [0.27–2.71]) than men (0.56 million [0.14–1.51]), largely but not entirely due to the higher life expectancy in women (age-standardized female-to-male ratio 1.19 [1.10–1.26]). Due to population aging, there was a large increase in all-age mortality rates from dementia between 1990 and 2019 (100.1% [89.1–117.5]). In 2019, deaths due to dementia ranked seventh globally in all ages and fourth among individuals 70 and older compared to deaths from other diseases estimated in the Global Burden of Disease (GBD) study. DISCUSSION: Mortality due to dementia represents a substantial global burden, and is expected to continue to grow into the future as an older, aging population expands globally

Use of multidimensional item response theory methods for dementia prevalence prediction: an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background: Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods: Using cognitive testing data and data on functional limitations from Wave A (2001–2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results: Our algorithm had a cross-validated predictive accuracy of 88% (86–90), and an area under the curve of 0.97 (0.97–0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3–4) in individuals 70–79, 11% (9–12) in individuals 80–89 years old, and 28% (22–35) in those 90 and older. Conclusions: Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys