搜索结果：Diseases of the colon and rectum

Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the target volume and adjacent organs of risks by segmentation is crucial to accomplish an optimal radiation distribution to the tumor while simultaneously preserving healthy tissue. Segmentation is performed manually and represents a time-consuming task in clinical daily routine. This study focuses on the segmentation of the rectum/sigmoid colon as an Organ-At-Risk in gynecological brachytherapy. The proposed segmentation method uses an interactive, graph-based segmentation scheme with a user-defined template. The scheme creates a directed two dimensional graph, followed by the minimal cost closed set computation on the graph, resulting in an outlining of the rectum. The graphs outline is dynamically adapted to the last calculated cut. Evaluation was performed by comparing manual segmentations of the rectum/sigmoid colon to results achieved with the proposed method. The com

The movement distribution, and volume of both chyme and gas in the colon, are important metrics to understand colonic function in health, disease, and the effects of treatments and different foodstuffs. Current methods available for assessment of these colonic contents using MRI consist mainly of manual segmentation or semi-automatic segmentation. However, these methods of segmentation are very labour intensive and too slow for clinical applications, require expert knowledge and some semi-automatic methods require use of bowel preparation. MRI scans were acquired in 2 breath holds using mDIXON sequences. We used the 'No New U-Net' (nnU-Net) ML model to automatically segment the colon, including colonic regions (ascending, transverse, descending and sigmoid-rectal). The ML-generated masks were corrected manually and the time taken for correction was recorded. ML segmentations were compared to both manual segmentations and observer corrected ML (CorrML) segmentations. Observer repeatability was also evaluated for both manual and CorrML methods to create a benchmark for the allowable error in the automatic segmentations. Analysis time was significantly reduced (p<0.0001) from 56 mi

Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in terms of area contraction and pressure generation. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.

Prion diseases are invariably fatal and highly infectious neurodegenerative diseases affecting humans and animals. By now there have not been some effective therapeutic approaches to treat all these prion diseases. In 2008, canine mammals including dogs (canis familials) were the first time academically reported to be resistant to prion diseases (Vaccine 26: 2601--2614 (2008)). Rabbits are the mammalian species known to be resistant to infection from prion diseases from other species (Journal of Virology 77: 2003--2009 (2003)). Horses were reported to be resistant to prion diseases too (Proceedings of the National Academy of Sciences USA 107: 19808--19813 (2010)). By now all the NMR structures of dog, rabbit and horse prion proteins had been released into protein data bank respectively in 2005, 2007 and 2010 (Proceedings of the National Academy of Sciences USA 102: 640--645 (2005), Journal of Biomolecular NMR 38:181 (2007), Journal of Molecular Biology 400: 121--128 (2010)). Thus, at this moment it is very worth studying the NMR molecular structures of horse, dog and rabbit prion proteins to obtain insights into their immunity prion diseases. This article reports the findings of th

Colon and rectum cancer share many risk factors, and are often tabulated together as ``colorectal cancer'' in published summaries. However, recent work indicating that exercise, diet, and family history may have differential impacts on the two cancers encourages analyzing them separately, so that corresponding public health interventions can be more efficiently targeted. We analyze colon and rectum cancer data from the Minnesota Cancer Surveillance System from 1998--2002 over the 16-county Twin Cities (Minneapolis--St. Paul) metro and exurban area. The data consist of two marked point patterns, meaning that any statistical model must account for randomness in the observed locations, and expected positive association between the two cancer patterns. Our model extends marked spatial point pattern analysis in the context of a log Gaussian Cox process to accommodate spatially referenced covariates (local poverty rate and location within the metro area), individual-level risk factors (patient age and cancer stage), and related interactions. We obtain smoothed maps of marginal log-relative intensity surfaces for colon and rectum cancer, and uncover significant age and stage differences b

Colonoscopy is the choice procedure to diagnose colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248,400 frames (47 videos), with depth annotations at the level of pix

Colonoscopy is the most common procedure for early detection and removal of polyps, a critical component of colorectal cancer prevention. Insufficient visual coverage of the colon surface during the procedure often results in missed polyps. To mitigate this issue, reconstructing the 3D surfaces of the colon in order to visualize the missing regions has been proposed. However, robustly estimating the local and global coverage from such a reconstruction has not been thoroughly investigated until now. In this work, we present a new method to estimate the coverage from a reconstructed colon pointcloud. Our method splits a reconstructed colon into segments and estimates the coverage of each segment by estimating the area of the missing surfaces. We achieve a mean absolute coverage error of 3-6\% on colon segments generated from synthetic colonoscopy data and real colonography CT scans. In addition, we show good qualitative results on colon segments reconstructed from real colonoscopy videos.

Colon cancer is a prevalent gastrointestinal malignancy arising in the colon. Ulcerative colitis(UC) is one of the risk factors of colorectal cancer. The detection of under-expressed biomarkers and molecular mechanisms in UC and colon cancer can lead to effective management of colitis-associated cancer. A total of two mRNA expression datasets (GSE87473 and GSE44076) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to screen differentially expressed genes (DEGs) between extensive ulcerative colitis samples and healthy samples, limited ulcerative colitis samples and healthy samples, and colon cancer samples and healthy samples. In extensive ulcerative colitis, limited ulcerative colitis and colon cancer groups, 95,69 and 635 under-expressed genes with adjusted p-value<0.05 and log(2) fold change<-2 were detected respectively. Using Cytoscape software, the genes with degree> 15 including CLCA1, SLC26A3, SI, KIT, HPGDS, NR1H4, ADIPOQ, PPARGC1A, GCG, MS4A12, GUCA2A and FABP1 were screened as hub under-expressed genes in colon cancer. In extensive ulcerative colitis, the genes with degree>5 including ABCB1, ABCG2, UGT1A6, CYP2B6 and AQP8 were id

A previous study of symmetric collisions of massive nuclei has shown that current models of multi-nucleon transfer (MNT) reactions do not adequately describe the transfer product yields. To gain further insight into this problem, we have measured the yields of MNT products in the interaction of 977 (E/A = 4.79 MeV) and 1143 MeV (E/A = 5.60 MeV) $^{204}$Hg with $^{208}$Pb. We find that the yield of multi-nucleon transfer products are similar in these two reactions and are substantially lower than those observed in the reaction of 1257 MeV (E/A = 6.16 MeV) $^{204}$Hg + $^{198}$Pt. We compare our measurements with the predictions of the GRAZING-F, di-nuclear systems (DNS) and improved quantum molecular dynamics (ImQMD) models. For the observed isotopes of the elements Au, Hg, Tl, Pb and Bi, the measured values of the MNT cross sections are orders of magnitude larger than the predicted values. Furthermore, the various models predict the formation of nuclides near the N=126 shell, which are not observed.

Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy ou

Univariate zero-inflated models are increasingly being used to account for excess zeros in spatio-temporal infectious disease counts. However, the multivariate case is challenging due to the need to account for correlations across space, time and disease in both the count and zero-inflated components of the model. We are interested in comparing the transmission dynamics of several co-circulating infectious diseases across space and time, where some of the diseases can be absent for long periods. We first assume there is a baseline disease that is well-established and always present in the region. The other diseases switch between periods of presence and absence in each area through a series of coupled Markov chains, which account for long periods of disease absence, disease interactions and disease spread from neighboring areas. Since we are mainly interested in comparing the diseases, we assume the cases of the present diseases in an area jointly follow an autoregressive multinomial model. We use the multinomial model to investigate whether there are associations between certain factors, such as temperature, and differences in the transmission intensity of the diseases. Inference

Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces and lesions, and facilitating the identification of unexplored areas. However, the development of robust methods is limited by the scarcity of large-scale ground truth data. We propose RealSynCol, a highly realistic synthetic dataset designed to replicate the endoscopic environment. Colon geometries extracted from 10 CT scans were imported into a virtual environment that closely mimics intraoperative conditions and rendered with realistic vascular textures. The resulting dataset comprises 28\,130 frames, paired with ground truth depth maps, optical flow, 3D meshes, and camera trajectories. A benchmark study was conducted to evaluate the available synthetic colon datasets for the tasks of depth and pose estimation. Results demonstrate that the high realism and variability of RealSynCol significantly enhance generalization performance on clinical images, proving it to be a powerful tool for developing deep learning algorithms to support endoscopic diagnosis.

The integration of empirical data in computational frameworks to model the spread of infectious diseases poses challenges that are becoming pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios and designing containment strategies. However, the integration of detailed data sources yields models that are less transparent and general. Hence, given a specific disease model, it is crucial to assess which representations of the raw data strike the best balance between simplicity and detail. We consider high-resolution data on the face-to-face interactions of individuals in a hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the contact patterns. We show that a contact matrix that only contains average contact durations fails to reproduce the size of the epidemic obtained with the high-resolution contact data and also to identify the most at-risk classes. We introduce a contact matrix of pro

Hirschsprung's disease (HD) is a congenital birth defect diagnosed by identifying the lack of ganglion cells within the colon's muscularis propria, specifically within the myenteric plexus regions. There may be advantages for quantitative assessments of histopathology images of the colon, such as counting the ganglion and assessing their spatial distribution; however, this would be time-intensive for pathologists, costly, and subject to inter- and intra-rater variability. Previous research has demonstrated the potential for deep learning approaches to automate histopathology image analysis, including segmentation of the muscularis propria using convolutional neural networks (CNNs). Recently, Vision Transformers (ViTs) have emerged as a powerful deep learning approach due to their self-attention. This study explores the application of ViTs for muscularis propria segmentation in calretinin-stained histopathology images and compares their performance to CNNs and shallow learning methods. The ViT model achieved a DICE score of 89.9% and Plexus Inclusion Rate (PIR) of 100%, surpassing the CNN (DICE score of 89.2%; PIR of 96.0%) and k-means clustering method (DICE score of 80.7%; PIR 77.

Hydroxyapatite nanoparticles (HANs) have emerged as a promising candidate for targeted drug delivery in the treatment of colon cancer, a disease that remains one of the leading causes of cancer-related deaths globally. In this study, we synthesized and characterized HANs for their potential use as drug delivery vehicle for anticancer drugs specifically targeting colon cancer treatment. Through various in vitro assays, including drug loading efficiency, release kinetics, we demonstrated that HANs exhibit efficient drug loading capacity, and sustained release behavior, which could collectively contribute to enhanced therapeutic outcomes against colon cancer cells. The findings highlight the potential of HANs to serve as a versatile platform for the targeted drug delivery of chemotherapeutic agents, offering a novel approach that could improve treatment efficacy while minimizing side effects due to biocompatibility of the particles. Future research should focus on in vivo studies and clinical translation to further validate the potential of HANs in cancer therapy as drug carrier.

Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using ICD-10. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interp

In this chapter, an application of Mathematical Epidemiology to crop vector-borne diseases is presented to investigate the interactions between crops, vectors, and virus. The main illustrative example is the cassava mosaic disease (CMD). The CMD virus has two routes of infection: through vectors and also through infected crops. In the field, the main tool to control CMD spreading is roguing. The presented biological model is sufficiently generic and the same methodology can be adapted to other crops or crop vector-borne diseases. After an introduction where a brief history of crop diseases and useful information on Cassava and CMD is given, we develop and study a compartmental temporal model, taking into account the crop growth and the vector dynamics. A brief qualitative analysis of the model is provided,i.e., existence and uniqueness of a solution,existence of a disease-free equilibrium and existence of an endemic equilibrium. We also provide conditions for local (global) asymptotic stability and show that a Hopf Bifurcation may occur, for instance, when diseased plants are removed. Numerical simulations are provided to illustrate all possible behaviors. Finally, we discuss the t

We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 secon

This paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how modern technology perpetuates and deepens societal inequalities. It specifically examines how AI is implicated in the exploitation of marginalized communities through underpaid labor in the gig economy, the perpetuation of biases in algorithmic decision-making, and the reinforcement of systemic barriers that prevent these groups from benefiting equitably from technological advances. Furthermore, the paper discusses the role of AI in extending and intensifying the social, economic, and psychological burdens faced by these communities, highlighting the problematic use of AI in surveillance, law enforcement, and mental health contexts. The analysis concludes with a call for transformative changes in how AI is developed and deployed. Advocating for a reevaluation of the values driving AI innovation, the paper promotes an approach that integrates social justice and equity