Pathology

Reduction of Acquisition Time in FTIR Spectroscopy via Spectral Super-Resolution by Deep Learning.

This study aims to enhance spectral resolution in Fourier transform infrared spectroscopy (FTIR), a crucial element for in-depth molecular analysis in clinical diagnosis. We address this challenge by developing spectral super-resolution models based on residual networks and U-Net (SSR-ResUNet), incorporating 1D, 2D, and 3D CNNs to reconstruct an image equivalent to a high-resolution IR spectral im...

Unraveling defect-mediated ion transport behavior in anti-perovskite solid-state electrolytes via machine learning molecular dynamics simulations.

Anti-perovskite (AP) solid-state electrolytes (SSEs) have emerged as promising candidates for high-safety solid-state batteries due to their wide electrochemical window and good compatibility with lithium metal anodes. However, their relatively low ionic conductivity significantly hinders their commercial application. Although defect engineering is considered a key strategy to enhance ionic conduc...

The long and winding road: the resilience of cervical cytology as a long-standing laboratory tool to aid the diagnosis of HPV-induced lesions.

BACKGROUND: Despite molecular advancements, cervical cytology remains a resilient and fundamental laboratory tool in the prevention of cervical cancer...

Precessional Dynamics of Octahedra in CsPbBr3.

The crystal structure of ABX3 halide perovskites consists of corner-sharing BX6 octahedra, whose collective distortions define the different crystallo...

Circulating extracellular vesicle long RNA profiling combined with machine learning unveils novel diagnostic signature and molecular features in chronic pancreatitis.

BACKGROUND: No clinically useful non-invasive biomarkers have been developed for diagnosis of chronic pancreatitis (CP), and molecular features of CP ...

A vision-language foundation model improves preoperative diagnosis of follicular thyroid neoplasms using ultrasound images.

Preoperative discrimination between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challenging, as imaging and cytolo...

Deep learning inference of cell type-specific gene expression from breast tumor histopathology.

Cell type-specific gene expression from single-cell RNA sequencing (RNA-seq) is valuable for breast cancer precision oncology but available cohorts ar...

Region-guided decoupled fusion network for ultrasound-based classification of thyroid nodules with and without Hashimoto's thyroiditis.

RATIONALE AND OBJECTIVES: Differentiating benign from malignant thyroid nodules is particularly challenging in patients with Hashimoto's thyroiditis (...

Morphology-informed deep learning for risk assessment of filamentous bulking in a full-scale industrial wastewater treatment plant.

Filamentous bulking (FB) is a recurring cause of solids-separation failure in activated sludge systems. Standard diagnostics such as sludge volume ind...

MUSIOMICS: A multi-region radiomics framework that outperforms single-region analysis in classifying malignant pulmonary nodules.

BACKGROUND AND OBJECTIVE: Radiomic studies in lung cancer have primarily analyzed single-domain features extracted separately from intranodular (Zone-...

Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation.

BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, and accurate histopathologic risk stratification is essential for treatment sel...

Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.

Optical volumetric imaging grapples with inherent noise problems arising from photon budget constraints, light scattering, and space-bandwidth product...

Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.

BACKGROUND: Ovarian cysts are a common pelvic disorder in women, and accurate differentiation between benign and malignant types is essential for guid...

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.

BACKGROUND: Accurate survival prediction for grade 2/3 glioma patients remains challenging due to tumor biological heterogeneity and limitations of cu...

Single-molecule microscopy of biomolecular condensates: From liquid droplets to heterogeneous networks.

Single-molecule microscopy has transformed our view of biomolecular condensates-membraneless organelles that organize cellular biochemistry and are fr...

Update on pleural mesothelioma.

PURPOSE OF REVIEW: Pleural mesothelioma remains a universally lethal cancer with a rising global burden. This underscores the need for pulmonologists ...

Recent Advances in Transducers for Through-Tissue Ultrasonic Power Transfer.

Ultrasonic power transfer (UPT) is gaining traction for wireless energy delivery to implants and wearables because it combines centimeter-scale penetr...

Recent Advances in Analytical Techniques for Cancer Diagnostics and Therapeutics: Combining State-of-the-Art Technologies for Precision Oncology.

Cancer remains a leading global health challenge, necessitating continuous advancements in diagnostics and therapeutics to enhance patient outcomes. A...

Deep learning-enhanced transparent transducer-based photoacoustic microscopy with improved SNR and resolution.

Transparent ultrasonic transducer enables compact coaxial photoacoustic microscopy, but the trade-off between optical transparency and sensitivity and...

A Method for the Detection of Tire Wear Microplastics in Zebrafish Guts by Laterally Resolved LA-ICP-MS-Based Elemental Fingerprinting and Chemometrics.

Tire wear particles (TWPs) are generated by mechanical abrasion of tires on road surfaces and represent a significant source of microplastic pollution...