AUNP-12 Near-Infrared Fluorescence Probes across NIR-I to NIR-II Enable Detection of PD-1/PD-L1 Axis in the Tumor Microenvironment.

The innovative PD-1/PD-L1 pathway strategy is gaining significant traction in cancer therapeutics. However, fluctuating response rates of 20-40% to PD-1/PD-L1 inhibitors, coupled with the risk of hyperprogression after immunotherapy, underscore the need for accurate patient selection and the identification of more beneficiaries. Molecular imaging, specifically near-infrared (NIR) fluorescence imaging, is a valuable alternative for real-time, noninvasive visualization of dynamic PD-L1 expression .

NIR-II light in clinical oncology: opportunities and challenges.

Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis.

Feasibility study of functional near-infrared spectroscopy in the ventral visual pathway for real-life applications.

Significance: fNIRS-based neuroenhancement depends on the feasible detection of hemodynamic responses in target brain regions. Using the lateral occipital complex (LOC) and the fusiform face area (FFA) in the ventral visual pathway as neurofeedback targets boosts performance in visual recognition. However, the feasibility of utilizing fNIRS to detect LOC and FFA activity in adults remains to be validated as the depth of these regions may exceed the detection limit of fNIRS.

Single-cell RNA sequencing in donor and end-stage heart failure patients identifies NLRP3 as a therapeutic target for arrhythmogenic right ventricular cardiomyopathy.

Background: Dilation may be the first right ventricular change and accelerates the progression of threatening ventricular tachyarrhythmias and heart failure for patients with arrhythmogenic right ventricular cardiomyopathy (ARVC), but the treatment for right ventricular dilation remains limited.

Methods: Single-cell RNA sequencing (scRNA-seq) of blood and biventricular myocardium from 8 study participants was performed, including 6 end-stage heart failure patients with ARVC and 2 normal controls. ScRNA-seq data was then deeply analyzed, including cluster annotation, cellular proportion calculation, and characterization of cellular developmental trajectories and interactions.

Diagnosing and grading gastric atrophy and intestinal metaplasia using semi-supervised deep learning on pathological images: development and validation study.

Objective: Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification.

Methods: In this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs).

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included.

NIR-II fluorescence-guided liver cancer surgery by a small molecular HDAC6 targeting probe.

Background: Hepatocellular carcinoma (HCC) is the sixth most common malignancy globally and ranks third in terms of both mortality and incidence rates. Surgical resection holds potential as a curative approach for HCC. However, the residual disease contributes to a high 5-year recurrence rate of 70%.

Transcutaneous Auricular Vagal Nerve Stimulation Is Effective for the Treatment of Functional Dyspepsia: A Multicenter, Randomized Controlled Study.

Introduction: Vagal nerve stimulation (VNS) can be used to modulate gastrointestinal motility, inflammation, and nociception. However, it remains unclear whether VNS is effective in adult patients with functional dyspepsia (FD). We investigated the effectiveness of transcutaneous auricular VNS (taVNS) in patients with FD.

Tumor Mutation Burden-Related Histopathologic Features for Predicting Overall Survival in Gliomas Using Graph Deep Learning.

Tumor mutation burden (TMB) is a potential biomarker for evaluating the prognosis and response to immune checkpoint inhibitors, but its costly and time-consuming method of measurement limits its widespread application. This study aimed to identify the TMB-related histopathologic features from hematoxylin and eosin slides and explore their prognostic value in gliomas. TMB-related features were detected using a graph convolutional neural network from whole-slide images of patients from The Cancer Genome Atlas data set (619 patients), and the correlation between features and TMB was evaluated in an external validation set (237 patients).

Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study.

Purpose: Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC).

Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis.

[This corrects the article DOI: 10.3389/fnins.2023.

Unraveling mitochondria-targeting reactive oxygen species modulation and their implementations in cancer therapy by nanomaterials.

Functional subcellular organelle mitochondria are emerging as a crucial player and driver of cancer. For maintaining the sites of cellular respiration, mitochondria experience production, and accumulation of reactive oxygen species (ROS) underlying oxidative damage in electron transport chain carriers. Precision medicine targeting mitochondria can change nutrient availability and redox homeostasis in cancer cells, which might represent a promising strategy for suppressing tumor growth.

Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis.

Visual expertise reflects accumulated experience in reviewing domain-specific images and has been shown to modulate brain function in task-specific functional magnetic resonance imaging studies. However, little is known about how visual experience modulates resting-state brain network dynamics. To explore this, we recruited 22 radiology interns and 22 matched healthy controls and used resting-state functional magnetic resonance imaging (rs-fMRI) and the degree centrality (DC) method to investigate changes in brain network dynamics.

PSSGAN: Towards spectrum shift based perceptual quality enhancement for fluorescence imaging.

Fluorescence imaging has demonstrated great potential for malignant tissue inspection. However, poor imaging quality of medical fluorescent images inevitably brings challenges to disease diagnosis. Though improvement of image quality can be achieved by translating the images from low-quality domain to high-quality domain, fewer scholars have studied the spectrum translation and the prevalent cycle-consistent generative adversarial network (CycleGAN) is powerless to grasp local and semantic details, leading to produce unsatisfactory translated images.

CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center.

Background: Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors.

Methods: This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma.

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking.

Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference.

Early stage of radiological expertise modulates resting-state local coherence in the inferior temporal lobe.

Background: The visual system and its inherent functions undergo experience-dependent changes through the lifespan, enabling acquisition of new skills. Previous fMRI studies using tasks reported increased specialization in a number of cortical regions subserving visual expertise. Although ample studies focused on representation of long-term visual expertise in the brain, i.

Artificial intelligence in gastric cancer: applications and challenges.

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies.

A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.

Background: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management.

Purpose: This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy.

Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study.

Background: This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction.

Methods: 274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR.