Multi-state model

Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. Here we used a "multi-omics" approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors...

UNLABELLED: The continued emergence of Neisseria gonorrhoeae strains that express resistance to multiple antibiotics, including the last drug for empiric monotherapy (ceftriaxone), necessitates the development of new treatment options to cure gonorrheal infections. Toward this goal, we recently reported that corallopyronin A (CorA), which targets the switch region of the β' subunit (RpoC) of bacterial DNA-dependent RNA polymerase (RNAP), has potent anti-gonococcal activity against a panel of multidrug-resistant clinical strains...

ISSUES: To date, there has been no synthesis of research addressing the scale and nuances of the opioid epidemic in racial/ethnic minority populations in the United States that considers the independent and joint impacts of dynamics such as structural disadvantage, provider bias, health literacy, cultural norms and various other risk factors. APPROACH: Using the "risk environment" framework, we conducted a scoping review on PubMed, Embase and Google Scholar of peer-reviewed literature and governmental reports published between January 2000 and February 2024 on the nature and scale of opioid use, opioid prescribing patterns, and fatal overdoses among racial/ethnic minorities in the United States, while also examining macro, meso and individual-level risk factors...

The abnormal aggregation of extracellular amyloid-<mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mi>β</mml:mi><mml:mspace/><mml:mo>(</mml:mo><mml:mi>A</mml:mi><mml:mi>β</mml:mi><mml:mo>)</mml:mo></mml:math> in senile plaques resulting in calcium <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mfenced><mml:mrow><mml:msup><mml:mrow><mml:mi>C</mml:mi><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:math> dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD)...

INTRODUCTION: Comprehensive sexuality education (CSE) is associated with positive sexual and reproductive health (SRH) outcomes, including increased contraceptive use, lower rates of unintended pregnancy and prevention of sexual violence. However, implementation of and requirements for CSE vary across the United States which can negatively impact students, both during and beyond high school, including among college students. METHODS: and Analysis: This paper describes the research protocol for a multi-staged approach for designing, implementing and evaluating an SRH course for up to 60 undergraduate students at a public university in California...

Previous study has demonstrated that the Dietary Inflammation Index (DII) played a role in the risk of inflammatory bowel disease (IBD), however, the prevalence and risk factors for IBD are distinct across locations and groups, and therefore, the findings are debatable and warrant further investigation. A total of 4363 participants were calculated in the National Health and Nutrition Examination Survey (NHANES) 2009 to 2010, of whom 1.21% self-reported a history of IBD. DII values were performed as a good predictor of dietary inflammation based on data from two 24-h dietary reviews in the NHANES database...

Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA...

Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information from a single dimensionality, resulting in sub-optimal performance on challenging magnetic resonance imaging (MRI) data with multiple segmentation objects and anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities...

BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models...

PURPOSE: Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal. METHODS: We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting...

Expression is the main method for judging the emotional state and psychological condition of the human body, and the prediction of changes in facial expressions can effectively determine the mental health of a person, thus avoiding serious psychological or psychiatric disorders due to early negligence. From a computer vision perspective, most researchers have focused on studying facial expression analysis, and in some cases, body posture is also considered. However their performance is more limited under unconstrained natural conditions, which requires more information to be used in human emotion analysis...

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition...

Human mesh recovery aims to estimate all human meshes within a given image. In this paper, we propose an Instance-aware Multi-person 3D Human Mesh Recovery (InstaHMR) network based on the one-stage framework. Compared to former one-stage methods, instance-aware single person feature is exploited to represent more accurate human mesh. Specifically, we propose the Contextual Instance Guidance (CIG) module which generates instance-aware single person feature by leveraging spatial and channel attention operations...

Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insignificant feature differences among numerous bones. In this study, we have proposed a novel deep learning-based solution for pediatric hand bone segmentation in the US. Our method is unique in that it allows for effective detailed feature mining through an adaptive multi-dimensional weighting attention mechanism...

Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there are limitations for Transformers in image fusion, such as lacks of local feature extraction and cross-modal feature interaction, resulting in insufficient multimodal feature extraction and integration. In addition, the computational cost of Transformers is higher...

In this article we present a quantitative analysis of the second positive system of molecular nitrogen and the first negative system of the molecular nitrogen cation excited in the presence of ionizing radiation. Optical emission spectra of atmospheric air and nitrogen surrounding 210 Po sources were measured from 250 to 400 nm. Multi-Boltzmann and non-Boltzmann vibrational distribution spectral models were used to determine the vibrational temperature and vibrational distribution function of the emitting N2 (C3 Πu ) and N2 + (B2 Σ+u) states...

CONTEXT: In order to study the relationship between the sensitivity and pressure of energetic materials, six kinds of energetic materials were selected as the research object. The crystal structure, electronic, and phonon properties under hydrostatic pressure of 0 ~ 45 GPa were calculated by first principles. The calculation results show that the lattice parameters and band gap values of these six energetic materials decrease with the increase of pressure. The peak of the density of states decreases and moves to the low energy direction, and the electrons become more active...

INTRODUCTION: With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction. METHODS: A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information...

Real-time detection of surgical tools in laparoscopic data plays a vital role in understanding surgical procedures, evaluating the performance of trainees, facilitating learning, and ultimately supporting the autonomy of robotic systems. Existing detection methods for surgical data need to improve processing speed and high prediction accuracy. Most methods rely on anchors or region proposals, limiting their adaptability to variations in tool appearance and leading to sub-optimal detection results. Moreover, using non-anchor-based detectors to alleviate this problem has been partially explored without remarkable results...

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package...