Tuberculosis

The role of medical imaging in translational research into early pulmonary tuberculosis.

Early detection of tuberculosis (TB) is central to global efforts for TB care and prevention. Conventional symptom-based screening and sputum microbiology fail to identify a substantial proportion of cases, particularly those that are asymptomatic or have low bacillary burden. Non-invasive medical imaging offers a critical solution for visualizing pulmonary pathology before individuals with TB dev...

Host-focused immunity, metabolism, and diagnostic innovation for precision control of tuberculosis globally.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains one of the deadliest infectious diseases worldwide. Despite advances in treatment and diagnostics, challenges such as multidrug resistance, inadequate vaccines, diagnostic gaps and health inequities continue to impede global control efforts. This review explores emerging insights into host-pathogen interactions, with emphasis o...

Machine learning methods for designing a carbon dot based photoluminescent multimodal nanosensor.

This study addresses the problem of simultaneous determination of metal cations and nitrate anions concentrations in multicomponent aqueous solutions....

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.

Tuberculosis Drug Discovery in the Age of Artificial Intelligence.

There is an urgent need to develop additional treatments for tuberculosis (TB) to complement the small panel of approved drugs and to devise shorter t...

MycoPermeNet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student Self-Distillation.

Machine learning (ML) techniques offer a promising path for accelerating antibiotic discovery by computationally predicting and optimizing desirable p...

Sustainable Strategies in Tuberculosis Management: Bridging Ethnobotanical Pharmacology, Advanced Drug Delivery, and AI-Driven Innovation.

Tuberculosis (TB) persists as a leading infectious disease, with progress in global control complicated by emerging drug resistance, limited access to...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Retrieval Augmented Generation (RAG) for Evaluating Regulatory Compliance of Drug Information and Clinical Trial Protocols.

The purpose was to evaluate retrieval-augmented generative (RAG) artificial intelligence (AI) methods for assessing the regulatory compliance of drug ...

Multidimensional cfRNA response modeling identifies a 5-gene pair signature for high-robust pulmonary tuberculosis diagnosis.

Lack of non-invasive biomarkers hinders pulmonary tuberculosis (PTB) management. We developed a multidimensional machine learning framework to systema...

A review of applications of machine learning in quantum dots research.

Machine learning (ML) is increasingly applied in quantum dot (QD) research to support data analysis, device control, materials optimization, sensing, ...

Rapid Prediction of Hot-Carrier Relaxation by Learning of Nonadiabatic Hamiltonians with Graph Neural Networks.

An electron-vibrational Hamiltonian fully encodes corresponding quantum dynamics; however, extracting the dynamics still relies on time and memory-con...

Data-driven model analysis of the impact of environmental and socioeconomic factors on tuberculosis incidence.

Tuberculosis (TB), a global infectious disease, poses a formidable challenge to Taiwan, China, exacerbated by its aging demographic and the incursion ...

Broadband Nanocavity Imaging with Machine Vision for Multiplex miRNA Assays.

Sensitive and multiplexed quantification of microRNAs (miRNAs) remains challenging due to their short length, low abundance, and sequence homology, pa...

Machine learning approaches to predict the risk of tuberculosis among household contacts of index TB patients in Central Ethiopia.

Compact deep neural network models of the visual cortex.

A powerful approach to understand the computations carried out by the visual cortex is to build models that predict neural responses to any arbitrary ...

Effectiveness of transcranial direct current stimulation (tDCS) on attention and memory biases in individuals with post-traumatic stress disorder (PTSD): a randomized, single-blind, parallel-group study.

Based on neurocognitive models, the development and maintenance of post-traumatic stress disorder (PTSD) are correlated with cognitive biases, includi...

Integrated in silico and in vitro screening approaches for the discovery of small-molecule inhibitors of β-carbonic anhydrases.

INTRODUCTION: β-Carbonic anhydrases (β-CAs) are zinc-dependent metalloenzymes that catalyze the reversible hydration of carbon dioxide to bicarbonate ...

From AI discovery to macroscopic formula: a quasi-stability criterion for quantum dots inspired by Elon Musk's vision.

With rapid advancement of artificial intelligence (AI), particularly the vision articulated by Elon Musk that AI can "discover new physics" and derive...

Assessment of Predictive Factors That Shorten Duration of Treatment in Patients With Multiple Myeloma Using AI: Real-World Longitudinal Study Using Data From Medical Data Vision Claims Database.

BACKGROUND: With the availability of newer therapies, the duration of therapy (DoT) shortens with each increasing line of treatment in Japanese patien...