Biomedical and Health Sciences
Nasal Cytology, or Rhinology, is the subfield of otolaryngology, focused on the microscope observation of samples of the nasal mucosa, aimed to recognize cells of different types, to spot and diagnose ongoing pathologies. Such methodology can claim good accuracy in diagnosing rhinitis and infections, being very cheap and accessible without any instrument more complex than a microscope, even optical ones.
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To address the challenges faced by patients with neurodegenerative disorders, Brain-Computer Interface (BCI) solutions are being developed. However, many current datasets lack inclusion of languages spoken by patients, such as Telugu, which is spoken by over 90 million people in India. To bridge this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of commonly used Telugu words. Using the Open-BCI Cyton device, EEG samples were captured from volunteers as they pronounced these words.
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The AnxiECG-PPG Database contains synchronized electrocardiogram (ECG) and mobile-acquired photoplethysmography (PPG) recordings from 47 healthy participants. Moreover, the acquisition protocol assesses three distinct states: a 5-minute Baseline, a 1-minute Physical Activated State, and a Psychological Activated state provoked through emotion-induced videos (negative, positive, and neutral emotion valence).
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Despite advances in vascular replacement and repair, fabricating small-diameter vascular grafts with low thrombogenicity and appropriate tissue mechanics remains a challenge. A wide range of platforms have been developed to use plant-derived scaffolds for various applications. Unlike animal tissue, plants are primarily composed of cellulose which can offer a promising, nonthrombogenic alternative capable of promoting cell attachment and redirecting blood flow.
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This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases.
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To access this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/doi/10.5281/zenodo.11711229
Please cite the following paper when using this dataset:
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taken from a manta-ro experiment in five psychiatric patients after physical remediation, and the images were cut (documented by the Ethics Committee, WHO)
This dataset has sorted out formats that are suitable for classification and recognition using CNN.
By drawing a mandala pattern, participants can project their inner emotions, potential beliefs, and inner worlds
This is a magical adventure that crosses the boundaries of the mind. This article will delve into the background, methods, and possible interpretations of the projected test for Mandalay painting
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A craniometry study was undertaken to obtain anthropometric measurements of three hundred and five (305) medical staff within Trinidad & Tobago which is a twin island republic situated in the Caribbean. A non-contact measurement method was used involving 3D scanning equipment to record the geometry of each subject’s head as a digital file. The digital files were then processed using CAD software to obtain measurements for twenty-two (22) facial points of interest. In addition, the gender of each staff member was recorded.
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Drug development is a process that is incredibly expensive and time-consuming. Computational drug repurposing can help to assign new indications for approved drugs. It is capable to reduce the cost of drug developments. Machine learning models have been introduced to repurpose drugs long before. Recent studies formulate computational drug repurposing problem as a latent link prediction task on a heterogenous network. A number of computational methods have been developed based on graph neural networks.
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