Learning

BACKGROUND: The TNFRSF9 molecule is pivotal in thyroid carcinoma (THCA) development. This study utilizes Pathomics techniques to predict TNFRSF9 expression in THCA tissue and explore its molecular mechanisms. METHODS: Transcriptome data, pathology images, and clinical information from the cancer genome atlas (TCGA) were analyzed. Image segmentation and feature extraction were performed using the OTSU's algorithm and pyradiomics package. The dataset was split for training and validation...

Alzheimer's disease (AD) is a fatal neurological illness that worsens with time. Preventing the aggregate formation of amyloid beta protein is a promising approach to treat Alzheimer's disease. This article describes an amiable procedure for the synthesis of Olesoxime-Resveratrol (OLX-RSV) encapsulated in exosomes. By suppressing Aβ1-42 aggregation and crossing the blood-brain barrier also known as BBB after intravenous treatment without resulting in any discernible damage, the nanocomposite demonstrated good biocompatibility...

Medical sciences education emphasizes basic science learning as a prerequisite to clinical learning. Studies exploring relationships between achievement in the basic sciences and subsequent achievement in the clinical sciences generally suggest a significant positive relationship. Basic science knowledge and clinical experience are theorized to combine to form encapsulated knowledge- a dynamic mix of information that is useful for solving clinical problems. This study explores the relationship between basic science knowledge (BSK), clinical science knowledge (CSK), and clinical problem-solving ability, as measured within the context of four veterinary colleges using both college-specific measures and professionally validated, standardized measures of basic and clinical science knowledge and problem-solving ability...

OBJECTIVES: To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. METHODS: This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0...

BACKGROUND: Refugees from Syria face health challenges with psychosocial dimensions due to disrupted networks, uncertain life situations, and language barriers. Additionally, a sedentary lifestyle increases the risk of noncommunicable diseases, so health-promoting initiatives involving physical activities are essential. PURPOSE: To explore physiotherapists' clinical reasoning and collaboration with refugee families in developing group-based health-promoting physical exercising to increase participants' wellbeing, sense of togetherness, and self-efficacy...

One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA)...

Cognitive control is a hallmark of human cognition. A large number of studies have focused on the plasticity of cognitive control and examined how repeated task experience leads to the improvement of cognitive control in novel task environments. However, it has been demonstrated that training-induced changes are very selective and that transfer occurs within one task paradigm but not across different task paradigms. The current study tested the possibility that cross-paradigm transfer would occur if a common cognitive control strategy is employed across different task paradigms...

BACKGROUND: Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary. AIM: Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques. METHOD: Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study...

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0...

RATIONALE: Intensely stressful experiences can lead to long-lasting changes in appetitive and aversive behaviors. In humans, post-traumatic stress disorder increases the risk of comorbid appetitive disorders including addiction and obesity. We have previously shown that an acute stressful experience in adult male rats suppresses motivation for natural reward. OBJECTIVES: We examine the impact of sex and age on the effects of intense stress on action-based (instrumental) and stimulus-based (Pavlovian) motivation for natural reward (food)...

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently...

No abstract text is available yet for this article.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression...

Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance...

BACKGROUND AND OBJECTIVE: Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerging ocular biomarkers from small imaging datasets. We sought to use machine teaching to automatically identify retinal ischemic perivascular lesions (RIPLs) and subretinal drusenoid deposits (SDDs), two emerging ocular biomarkers of cardiovascular disease. MATERIALS AND METHODS: IRB approval was obtained...

We report a deep learning-based approach to accurately predict the emission spectra of phosphorescent heteroleptic [Ir(C^N)2(NN)]+ complexes, enabling the rapid discovery of novel Ir(III) chromophores for diverse applications including organic light-emitting diodes and solar fuel cells. The deep learning models utilize graph neural networks and other chemical features in architectures that reflect the inherent structure of the heteroleptic complexes, composed of C^N and N^N ligands, and are thus geared towards efficient training over the dataset...

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry...

We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔ G ‡ of the enantiomeric reaction paths...

Practical acoustic propagation modeling is significantly affected by ocean dynamics, and then can be exploited in numerous oceanic applications, where "practical" refers to modeling acoustic propagation in simulations that mimic real-world ocean environments. Physics-based numerical models provide approximate solutions of wave equation and rely on accurate prior environmental knowledge while the environment of practical scenarios is largely unknown. In contrast, data-driven machine learning offers a promising avenue to estimate practical acoustic propagation by learning from dataset...

No abstract text is available yet for this article.