AIMC Topic: Humans

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iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.

Interdisciplinary sciences, computational life sciences
Enhancers are short DNA fragments capable of significantly increase the frequency of gene transcription. They often exert their effects on targeted genes over long distances, either in cis or in trans configurations. Identifying enhancers poses a cha...

Systemic inflammation mediates the relationship between urinary cadmium and chronic cough risk: findings based on multiple statistical models.

Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine
Epidemiological research examining the relationship between urinary cadmium and the risk of chronic cough remains scarce. This study included 2965 participants for a cross-sectional study from the NHANES. The weighted quantile sum (WQS) regression, b...

Detection of Negative Emotions in Short Texts Using Deep Neural Networks.

Cyberpsychology, behavior and social networking
Emotion detection is crucial in various domains, including psychology, health, social sciences, and marketing. Specifically, in psychology, identifying negative emotions in short Spanish texts, such as tweets, is vital for understanding individuals' ...

Learning Emotion Category Representation to Detect Emotion Relations Across Languages.

IEEE transactions on pattern analysis and machine intelligence
Understanding human emotions is crucial for a myriad of applications, from psychological research to advancements in Natural Language Processing (NLP). Traditionally, emotions are categorized into distinct basic groups, which has led to the developme...

Bridging Spectral Gaps: Cross-Device Model Generalization in Blood-Based Infrared Spectroscopy.

Analytical chemistry
This paper presents a solution to the challenge of cross-device model generalization in blood-based infrared spectroscopy. As infrared spectroscopy becomes increasingly popular for analyzing human blood, ensuring that machine learning models trained ...

Precise Electromagnetic Modulation of the Cell Cycle and Its Applications in Cancer Therapy.

International journal of molecular sciences
Precise modulation of the cell cycle via electromagnetic (EM) control presents a groundbreaking approach for cancer therapy, especially in the development of personalized treatment strategies. EM fields can precisely regulate key cellular homeostatic...

Modification of the toronto rehabilitation institute-hand function test for integration into robot-assisted therapy: technical validation and usability.

Biomedical engineering online
BACKGROUND: Effective rehabilitation of the upper extremity function is vital for individuals recovering from stroke or cervical spinal cord injury, as it can enable them to regain independence in daily tasks. While robotic therapy provides precise a...

Performance of single-agent and multi-agent language models in Spanish language medical competency exams.

BMC medical education
BACKGROUND: Large language models (LLMs) like GPT-4o have shown promise in advancing medical decision-making and education. However, their performance in Spanish-language medical contexts remains underexplored. This study evaluates the effectiveness ...

Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.

BMC medical imaging
Medical images occupy the largest part of the existing medical information and dealing with them is challenging not only in terms of management but also in terms of interpretation and analysis. Hence, analyzing, understanding, and classifying them, b...

A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study.

BMC medicine
BACKGROUND: CT images and circulating tumor cells (CTCs) are indispensable for diagnosing the mediastinal lesions by providing radiological and intra-tumoral information. This study aimed to develop and validate a deep multimodal fusion network (DMFN...