Biology Methods and Protocols

Individual organizations, such as hospitals, pharmaceutical companies and health insurance providers are currently limited in their ability to collect data that is fully representative of a disease population. This can in turn negatively impact the generalization ability of statistical models and scientific insights. However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally...

When comparing the behavioral levels of men and women using a maze navigation game created on a PC to investigate spatial cognitive function, many reports indicated that men are significantly faster than women at finding the exit of the maze. Analysis with functional MRI neuroimaging indicates that neural activation at multiple sites were observed during a maze navigation task. Significantly different regions were identified in men and women, with men showing significantly increased activity in...

COVID-19 affects a variety of organs and systems of the body including the central nervous system. Recent research has shown that COVID-19 survivors often experience neurological and psychological complications that can last for months after infection. We conducted a large internet study using online tests to analyze the effects of SARS-CoV-2 infection, COVID-19 severity, and vaccination on health, intelligence, memory, and information processing precision and speed in a cohort of 4,446 subjects...

The long-term consequences of COVID-19 infection are becoming increasingly evident in recent studies. This repeated cross-sectional study aimed to explore the long-term health and cognitive effects of COVID-19, focusing on how virus variants, vaccination, illness severity, and time since infection impact post-COVID-19 outcomes. We examined three cohorts of university students (N=584) and used non-parametric methods to assess correlations of various health and cognitive variables with SARS-CoV-2 ...

It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to...

BackgroundPostoperative delirium (POD) is a complication of surgery in older adults associated with adverse outcomes. Current screening methods demonstrate poor interrater reliability, and conventional electroencephalography (EEG)-based screening requires intensive setup. Point-of-care (POC) EEG technology offers a rapid and objective alternative that may capture neurophysiological signatures of delirium risk. When combined with baseline and perioperative variables, POC EEG may enable prediction...

Accurate classification of pediatric dental diseases from panoramic radiographs is crucial for early diagnosis and treatment planning. This study explores a text-based approach using a natural language transformer to generate textual descriptions of radiographs, which are then classified using deep learning models. Three models were evaluated: a one-dimensional convolutional neural network (1D-CNN), a long short-term memory (LSTM) network, and a pretrained bidirectional encoder representations f...

BackgroundThe near-exponential increase in the number of publications in orthodontics poses a challenge for efficient literature appraisal and evidence-based practice. Language models (LM) have the potential, through their question-answering fine-tuning, to assist clinicians and researchers in critical appraisal of scientific information and thus to improve decision-making. MethodsThis paper introduces OrthodonticQA (OQA), the first question-answering dataset in the field of dentistry which is ...

One-third of humanity harbors a lifelong infection with Toxoplasma gondii. This parasite undergoes sexual reproduction in cats and asexual reproduction in any warm-blooded intermediate hosts. The cycle progresses as cats ingest these hosts, containing the parasites tissue cysts. Such infections can alter behaviors in both animals and humans, potentially increasing predation risk by felines--usually seen as parasite-induced manipulations. This study aims to delineate toxoplasmosiss effects on cog...

BackgroundThe number of elective surgeries for older individuals is on the rise globally. Machine learning may improve risk assessment with an impact on surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early identification of postoperative delirium (POD). This study aims to estimate the predictive ability of machine learning models for POD using pre-and/or perioperative features, with a specific focus on adding neuropsychological assessments prior to sur...

The COVID-19 pandemic highlighted the need for effective protection and rapid development of tests to track and quantify seroconversion through natural infection and vaccination. Recombinant proteins, consisting of the SARS-CoV-2 nucleocapsid and Spike Receptor Binding Domains (RBD) fused with nanoluciferase reporters (GloBodies) were designed and produced. The SARS-CoV-2 specific antibody within serum, from venous blood or eluted from local or remotely-obtained dried blood spots, form a complex...

ObjectivesPatient-specific multiscale modeling simulates virtual surgeries of the Fontan procedure using three different graft options. Predictive modeling details post-operative outcomes that can help inform clinical decision support. MethodsSix patients underwent preoperative cardiac magnetic resonance imaging and catheterization. Virtual surgery is carried out for each patient to test the resulting hemodynamics of three Fontan graft options: ECC, 9mm Y-graft, and 12mm Y-graft. Results1) one...

Intra-operative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop a deep learning-based model to predict the pathologic margin status of resected breast tumors using specimen mammography. A dataset of specimen mammography images matched with pathology reports describing margin status was collected. Mo...

Skin cancer, also known as melanoma, is generally diagnosed visually from the dermoscopic images, which is a tedious and time-consuming task for the dermatologist. Such a visual assessment, via the naked eye for skin cancers, is a challenging and arduous due to different artifacts such as low contrast, various noise, presence of hair, fiber, and air bubbles, etc. This article proposes a robust and automatic framework for the Skin Lesion Classification (SLC), where we have integrated image augmen...

Melanoma remains the most lethal form of skin cancer, necessitating early detection for optimal patient outcomes. This study presents an advanced automated diagnostic system utilizing state-of-the-art convolutional neural networks--NFNet-L0, ResNeSt-101e, and MogaNet-XT--to classify nine types of cutaneous lesions from dermoscopic images. Leveraging a diverse dataset of 22,618 images from the International Skin Imaging Collaboration (ISIC) and Venezuelan clinical centers, the system demonstrates...

ObjectiveClustering methods are often applied to electronic medical records (EMR) for various objectives, including the discovery of previously unrecognized disease subtypes. The abundance and redundancy of information in EMR data raises the need to rank the features by their relevance to clustering. MethodsHere we propose FRIGATE, an ensemble feature ranking algorithm for clustering. FRIGATE ranks the features by solving multiple clustering problems on subgroups of features, using game-theoret...

By generating realistic histologically-stained images from label-free image data, virtual staining (VS) methods have the potential to streamline clinical workflows, improve image consistency and provide new ways of visualizing and analysing tissues. This article describes a new VS approach based on the application of conditional generative adversarial networks to translate high-resolution phase images of unstained tissues, recovered using Fourier ptychographic microscopy (FPM), into brightfield ...

Background/ObjectiveThe reliability of electronic medical records (EMR) for exposure to sedative and analgesic medications in patients with acute disorders of consciousness is unknown. Our objective was to quantify the accuracy of sedative and analgesic infusion rates derived from the EMR to support its use in big data clinical research. MethodsWe conducted a prospective cohort study enrolling critically ill patients who were unresponsive to verbal commands after acute brain injury. During stan...

ObjectivesThis systematic review aims to provide a comprehensive overview of the current state of research on the application of transformers in skin lesion classification. Materials and MethodsOver the period 2017-2023, this systematic review investigated the application of transformer-based models in skin lesion classification, focusing on 57 articles retrieved from prominent databases which are PubMed, Scopus, and Medline. The inclusion criteria encompass studies centering on transformer-bas...

ImportanceEarly recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries. ObjectiveTo develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery. Design, setting, and participantsML models, referred as Roux-MMC model, were developed and validated using a retrospective cohort e...