Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.
Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.
Research Group Biotechnology of Nutraceuticals and Bioactive Compounds (BIONUC), Departamento de Biología Funcional, Área de Microbiología, University of Oviedo, 33006 Oviedo, Principality of Asturias, Spain; and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Instituto de Investigación Sanitaria del Principado de Asturias (IISPA), 33011 Oviedo, Asturias, Spain.
Research Group Biotechnology of Nutraceuticals and Bioactive Compounds (BIONUC), Departamento de Biología Funcional, Área de Microbiología, University of Oviedo, 33006 Oviedo, Principality of Asturias, Spain; and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Instituto de Investigación Sanitaria del Principado de Asturias (IISPA), 33011 Oviedo, Asturias, Spain.
Research Group Biotechnology of Nutraceuticals and Bioactive Compounds (BIONUC), Departamento de Biología Funcional, Área de Microbiología, University of Oviedo, 33006 Oviedo, Principality of Asturias, Spain; and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Instituto de Investigación Sanitaria del Principado de Asturias (IISPA), 33011 Oviedo, Asturias, Spain.
Research Group Biotechnology of Nutraceuticals and Bioactive Compounds (BIONUC), Departamento de Biología Funcional, Área de Microbiología, University of Oviedo, 33006 Oviedo, Principality of Asturias, Spain; and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Instituto de Investigación Sanitaria del Principado de Asturias (IISPA), 33011 Oviedo, Asturias, Spain.
BioDyMIA (Bioingénierie et Dynamique Microbienne aux Interfaces Alimentaires), EMA 3733, Univ Lyon, Université Claude Bernard Lyon 1, Isara Lyon, Bourg en Bresse, France.
BioDyMIA (Bioingénierie et Dynamique Microbienne aux Interfaces Alimentaires), EMA 3733, Univ Lyon, Université Claude Bernard Lyon 1, Isara Lyon, Bourg en Bresse, France.
Post Harvest Management of Meat, Poultry and Fish, Post Graduate Institute of Post Harvest Management (Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli), Raigad, Maharashtra, India.
Post Harvest Management of Meat, Poultry and Fish, Post Graduate Institute of Post Harvest Management (Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli), Raigad, Maharashtra, India.
In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs agains...
Surgical data science is an emerging field focused on quantitative analysis of pre-, intra-, and postoperative patient data (Maier-Hein et al. in Med Image Anal 76: 102306, 2022). Data science approac...
Endoscopic video recordings of transsphenoidal pituitary tumor removal surgeries were collected from a multicenter research collaborative. These videos were anonymized and stored in a cloud-based plat...
A fully annotated video of a transsphenoidal pituitary tumor removal surgery was produced. This annotated video included over 129,826 frames. To prevent any missing annotations, all frames were later ...
A standardized and reproducible workflow for managing surgical video data is a necessary prerequisite to surgical data science applications. We developed a standard methodology for annotating surgical...
Advancements in clinical treatment are increasingly constrained by the limitations of supervised learning techniques, which depend heavily on large volumes of annotated data. The annotation process is...
The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of var...
Discovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the glo...
In this article, we employed eight different ML algorithms namely, extreme gradient boosting, random forest, gradient boosting classifier, deep neural network, support vector machine, multilayer perce...
The top four ML classifiers (extreme gradient boosting, random forest, gradient boosting classifier and deep neural network) were able to achieve an accuracy of 80 per cent and above during the evalua...
We aggregated the top performing four models through a soft-voting technique to develop an ensemble-based ML method and incorporated it into a freely accessible online prediction server named ABDpred ...
Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can de...
Hoarding disorder (HD) is characterized by a compulsion to collect belongings, and to experience significant distress when parting from them. HD is often misdiagnosed for several reasons. These includ...
Five hundred online participants were randomly recruited and completed the Hoarding Rating Scale-Self Report (HRS-SR) and the Generalized Anxiety Disorder 7-item (GAD-7) scale. Responses to the questi...
According to the psychiatrists, approximately 10% of the participants fulfilled DSM-5 diagnostic criteria for HD. 93% of the clinician-identified cases were identified by the ML model based on HRS-SR ...
Study findings strongly suggest that ML can, in the future, play a significant role in the risk assessment of psychiatric disorders prior to face-to-face consultation. By using AI to scan big data que...
Effective and standardized monitoring methodologies are vital for successful reservoir restoration and management. Environmental DNA (eDNA) metabarcoding sequencing offers a promising alternative for ...
Suicide is a global public health issue causing around 700,000 deaths worldwide each year. Therefore, identifying suicidal thoughts and behaviors in patients can help lower the suicide-related mortali...
We conducted a systematic search on PubMed, Scopus, and Web of Science to identify studies examining suicidality by applying ML methods to MRI features. Also, the Prediction Model Risk of Bias Assessm...
23 studies met the inclusion criteria. Of these, 20 developed prediction models without external validation and 3 developed prediction models with external validation. The performance of ML models var...
Small sample sizes, lack of external validation, heterogeneous study designs, and ML model development....
Most of the studies developed ML models capable of ML-based suicide identification, although ML models' predictive performance varied across the reviewed studies. Thus, further well-designed is necess...
Immune system dysfunction is hypothesised to contribute to structural brain changes through aberrant synaptic pruning in schizophrenia. However, evidence is mixed and there is a lack of evidence of in...
The total sample consisted of 1067 participants (chronic patients with schizophrenia n = 467 and healthy controls (HCs) n = 600) from the Australia Schizophrenia Research Bank (ASRB) dataset, together...
An optimal clustering solution revealed five main schizophrenia groups separable from HC: Low Inflammation, Elevated CRP, Elevated IL-6/IL-8, Elevated IFN-γ, and Elevated IL-10 with an adjusted Rand i...
Inflammation in schizophrenia may not be merely a case of low vs high, but rather there are pluripotent, heterogeneous mechanisms at play which could be reliably identified based on accessible, periph...
