Recent Advances in Strategies to Enhance Photodynamic and Photothermal Therapy Performance of Single-Component Organic Phototherapeutic Agents.

Photodynamic therapy (PDT) and photothermal therapy (PTT) have emerged as promising treatment options, showcasing immense potential in addressing both oncologic and nononcologic diseases. Single-component organic phototherapeutic agents (SCOPAs) offer advantages compared to inorganic or multicomponent nanomedicine, including better biosafety, lower toxicity, simpler synthesis, and enhanced reproducibility. Nonetheless, how to further improve the therapeutic effectiveness of SCOPAs remains a challenging research area.

Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology.

Purpose: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.

Methods: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score.

Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.

Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.

The clinical diagnostic value of right-to-left shunt in cryptogenic stroke under right heart contrast echocardiography: a retrospective case-control study.

Background: About 30% of ischemic strokes do not have a clear cause, which is called cryptogenic stroke (CS). Increasing evidence suggests a potential link between CS and right-to-left shunt (RLS). RLS may lead to CS via paradoxical embolic mechanism.

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.

New technologies for future of surgery in Ireland: An RCSI working Group report 2024.

Introduction: Surgery is a cognitive discipline whose practitioners characteristically use technology during operations for patients. With accelerating technological innovation throughout society and healthcare, we sought to develop a shared position for Irish surgery via a commissioned work programme by the Royal College of Surgeons in Ireland.

Methods: Using Stanford design principles, representative clinical specialty and academic leads and higher trainee representatives across 15 specialties were surveyed regarding sentiments, perspectives and concerns regarding now and near future technology in clinical practice, career considerations and training/education.

Longitudinal Twin Growth Discordance Patterns and Adverse Perinatal Outcomes.

Objective: The objective of this study was to conduct a longitudinal assessment of inter-twin growth and Doppler discordance, to identify possible distinct patterns, and to investigate the predictive value of longitudinal discordance patterns for adverse perinatal outcomes in twin pregnancies.

Methods: This retrospective cohort study included twin pregnancies followed and delivered at a tertiary University Hospital in London (UK), between 2010 and 2023. We included pregnancies with at least three ultrasound assessments after 18 weeks and delivery after 34 weeks' gestation.

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.

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.

SEE: A Method for Predicting the Dynamics of Chromatin Conformation Based on Single-Cell Gene Expression.

The dynamics of chromatin conformation involve continuous and reversible changes within the nucleus of a cell, which participate in regulating processes such as gene expression, DNA replication, and damage repair. Here, SEE is introduced, an artificial intelligence (AI) method that utilizes autoencoder and transformer techniques to analyze chromatin dynamics using single-cell RNA sequencing data and a limited number of single-cell Hi-C maps. SEE is employed to investigate chromatin dynamics across different scales, enabling the detection of (i) rearrangements in topologically associating domains (TADs), and (ii) oscillations in chromatin interactions at gene loci.

Paired nerve stimulation with selective compensation effect.

Background: In this study we investigate the selective compensation of paired peripheral nerves in healthy humans, focusing on distinct axonal conduction velocities in different fibre types. Using paired associative stimulation (PAS) with adjustable parameters, we aimed to modulate and compensate for neuronal activity along the median nerve.

Methods: Six healthy volunteers (3 male, 3 female, aged: 22-49) participated in the current study.

An overview of artificial intelligence based automated diagnosis in paediatric dentistry.

Artificial intelligence (AI) is a subfield of computer science with the goal of creating intelligent machines (1) Machine learning is a branch of artificial intelligence. In machine learning a datasets are used for training diagnostic algorithms. This review comprehensively explains the applications of AI in the diagnosis in paediatric dentistry.

Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.

The Natural Product Research Laboratory (NPRL) of China Medical University Hospital (CMUH) was established in collaboration with CMUH and Professor Kuo-Hsiung Lee from the University of North Carolina at Chapel Hill. The laboratory collection features over 6000 natural products worldwide, including pure compounds and semi-synthetic derivatives. This is the most comprehensive and fully operational natural product database in Taiwan.