Applying Natural Language Processing Techniques to Map Trends in Insomnia Treatment Terms on the r/Insomnia Subreddit: Infodemiology Study.

Background: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treatments.

Objective: The aim of this study is to summarize and chart trends of insomnia treatment terms on a digital insomnia message board.

Molecular dynamics in pharmaceutical nanotechnology: simulating interactions and advancing applications.

Molecular Dynamics (MD) simulations are now widely utilized in pharmaceutical nanotechnology to gain deeper understanding of nanoscale processes imperative to drug design. This review has also detailed how MD simulation can be employed in the study of drug-nanocarrier interactions, controlling release of chemical compounds from drug delivery systems and increasing solubility and bioavailability of nanocarriers. Furthermore, MD contributes to examining the drug delivery systems, measuring the toxic effects, and determining biocompatibility of nanomedical systems.

Biomarkers.

Background: A key characteristic of Alzheimer's disease (AD) is cerebral aggregation of tau. These aggregates can be quantified and localized with positron emission tomography (PET), which improves the diagnostic and prognostic work-up of AD. However, tau-PET is expensive and not available in clinical settings globally.

Biomarkers.

Background: The buildup of brain amyloid-beta and tau protein aggregates do not sufficiently explain the heterogeneity in cognitive impairment in Alzheimer's disease (AD).

Method: To elucidate drivers of cognitive impairment, we measure the levels of 7,000 proteins, in addition to amyloid-beta-42 (Ab42) and phospho-tau-181 (PTau181), from the cerebrospinal fluid of 2,000 individuals from healthy to severe dementia.

Result: We identify synapse proteins as the strongest correlates of cognitive impairment.

Biomarkers.

Background: PET quantification using the Standardised Uptake Value Ratio (SUVR) relies on the availability of a robust reference region. Intrinsic noise, spill in, and specific binding in the reference region can impact the reliability of the resulting SUVR. We evaluate a novel deep learning method trained on longitudinal data that penalises unexpected temporal changes and learns a SUVR correction factor that compensates for any noise or bias in the reference region, resulting in an improved quantification.

Biomarkers.

Background: There is an urgent need for neuropsychological screening tests that are easily deployed and reliable. We have developed a digital neuropsychological screening protocol that is administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer. This tablet-administered protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illness METHOD: The digital protocol was administered to 77 ambulatory care/ memory clinic patients (Table 1).

Biomarkers.

Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.

Biomarkers.

Background: Diagnostic and prognostic decisions about Alzheimer's disease (AD) are more accurate when based on large data sets. We developed and validated a machine learning (ML) data harmonization tool for aggregation of prospective data from neuropsychological tests applied to study AD. The online ML-combine application (OML-combine app) allows researchers to utilize the ML-harmonization method for harmonization of their own data with that from other large available data bases (e.

Public Health.

Background: The number people living with Alzheimer's disease and related dementias is expected to triple by 2050, contributing to decreased quality of life, increased medical care utilization, and additional burden on an already stressed primary care system. Many clinicians lack confidence to assess, diagnose and manage cognitive impairment (CI), and more than 50% of patients with CI are undiagnosed. A tool to better identify patients at risk of CI could lead to earlier detection and diagnosis in primary care.

Biomarkers.

Background: Semantic memory refers to knowledge of attributes associated with common objects. Quantifying the strength of semantic association between successive 'animal' fluency responses can be challenging. The current research assessed between-group differences for 'animal' fluency total output and selected verbal serial list learning, episodic memory measures.

Alzheimer's Imaging Consortium.

Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.

Technology and Dementia Preconference.

Background: Semantic memory refers to knowledge of attributes associated with common objects. Quantifying the strength of semantic association between successive 'animal' fluency responses can be challenging. The current research assessed between-group differences for 'animal' fluency total output and selected verbal serial list learning, episodic memory measures.

Technology and Dementia Preconference.

Background: There is an urgent need for neuropsychological screening tests that are easily deployed and reliable. We have developed a digital neuropsychological screening protocol that is administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer. This tablet-administered protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illness METHOD: The digital protocol was administered to 77 ambulatory care/ memory clinic patients (Table1).

Presence of Fragmented Intratumoral Thrombosed Microvasculature in the Necrotic and Peri-Necrotic Regions on SWI Differentiates IDH Wild-Type Glioblastoma From IDH Mutant Grade 4 Astrocytoma.

Background: Isocitrate dehydrogenase (IDH) wild-type (IDH) glioblastomas (GB) are more aggressive and have a poorer prognosis than IDH mutant (IDH) tumors, emphasizing the need for accurate preoperative differentiation. However, a distinct imaging biomarker for differentiation mostly lacking. Intratumoral thrombosis has been reported as a histopathological biomarker for GB.

AI Thinking: a framework for rethinking artificial intelligence in practice.

Artificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualizations of AI in practice and help shape the future of AI use.

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

Accurate predictions on small data with a tabular foundation model.

Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.

A foundation model of transcription across human cell types.

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types.

A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.

The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals.

Boosting skin cancer diagnosis accuracy with ensemble approach.

Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced.

Identification and validation of a metabolic-related gene risk model predicting the prognosis of lung, colon, and breast cancers.

Metabolic reprogramming, vital for cancer cells to adapt to the altered microenvironment, remains a topic requiring further investigation for different tumor types. Our study aims to elucidate shared metabolic reprogramming across breast (BRC), colorectal (CRC), and lung (LUC) cancers. Leveraging gene expression data from the Gene Expression Omnibus and various bioinformatics tools like MSigDB, WebGestalt, String, and Cytoscape, we identified key/hub metabolism-related genes (MRGs) and their interactions.

Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.

Objective: To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.

Methods: We included participants of the population-based Imaging in Lifelines (ImaLife) study with low-dose chest CT at baseline and 3-4 month follow-up. An AI system (AI-Rad Companion chest CT prototype) performed automatic segmentation and quantification of vertebral height and density, aortic diameters, heart volume (cardiac chambers plus pericardial fat), and coronary artery calcium volume (CACV).

Impurity detection of premium green tea based on improved lightweight deep learning model.

Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in this industry, this article uses deep learning technology to detect impurities in premium green tea.

Implementation of machine learning tool for continued process verification of process chromatography unit operation.

Recent advancements in technology, such as the emergence of artificial intelligence (AI) and machine learning (ML), have facilitated the progression of the biopharmaceutical industry toward the implementation of Industry 4.0. As per the guidelines set by the USFDA, process validation for biopharmaceutical production consists of three stages: process design, process qualification, and continuous process verification (CPV).

Cross-center Model Adaptive Tooth segmentation.

Automatic 3-dimensional tooth segmentation on intraoral scans (IOS) plays a pivotal role in computer-aided orthodontic treatments. In practice, deploying existing well-trained models to different medical centers suffers from two main problems: (1) the data distribution shifts between existing and new centers, which causes significant performance degradation. (2) The data in the existing center(s) is usually not permitted to be shared, and annotating additional data in the new center(s) is time-consuming and expensive, thus making re-training or fine-tuning unfeasible.