Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer.

Introduction: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone.

Prognostic factors and adjuvant chemotherapy efficacy in stage I gastric cancer patients: a retrospective analysis.

Background: Although stage I gastric cancer (GC) presents a favorable survival rate, outcomes for patients experiencing recurrence remain poor. This research focuses on assessing the prognosis and identifying risk factors for stage I GC patients, further assessing the necessity of adjuvant chemotherapy (AC).

Methods: The study involved a retrospective analysis of 902 patients with stage I GC who received curative resection from November 2010 to December 2020.

A prediction model based on dual-layer spectral detector computed tomography for distinguishing nonluminal from luminal invasive breast cancer.

Background: The identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer.

Methods: A total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study.

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Introduction: This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

Methods: In this retrospective study, a total of 371 patients diagnosed with clinical stage I solid LADC were randomly categorized into training and validation sets in a 7:3 ratio. Uni- and multivariate logistic regression analyses were performed to examine the imaging findings that can be used to predict pathological high-grade patterns meticulously.

MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer.

Purpose: Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection.

Methods: This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n = 84) and a validation set (n = 42).

Enhanced MobileNet for skin cancer image classification with fused spatial channel attention mechanism.

Skin Cancer, which leads to a large number of deaths annually, has been extensively considered as the most lethal tumor around the world. Accurate detection of skin cancer in its early stage can significantly raise the survival rate of patients and reduce the burden on public health. Currently, the diagnosis of skin cancer relies heavily on human visual system for screening and dermoscopy.

Injecting competition into online programming and Chinese- English translation classrooms.

The introduction of competition has the potential to enhance the efficacy of students' learning performance. Nevertheless, there have been contradictory findings about the impact of intergroup competition on students' learning performance and engagement. Therefore, further comprehensive investigations for this problem are necessary.

Corrigendum: Music-evoked emotions classification using vision transformer in EEG signals.

[This corrects the article DOI: 10.3389/fpsyg.2024.

Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model.

Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features.

The role of novel protein acylations in cancer.

Novel protein acylations are a class of protein post-translational modifications, such as lactylation, succinylation, crotonylation, palmitoylation, and β-hydroxybutyrylation. These acylation modifications are common in prokaryotes and eukaryotes and play pivotal roles in various key cellular processes by regulating gene transcription, protein subcellular localization, stability and activity, protein-protein interactions, and protein-DNA interactions. The diversified acylations are closely associated with various human diseases, especially cancer.

Corrigendum: Music-evoked emotions classification using vision transformer in EEG signals.

[This corrects the article DOI: 10.3389/fpsyg.2024.

Body composition and inflammation variables as the potential prognostic factors in epithelial ovarian cancer treated with Olaparib.

Background: Epithelial ovarian cancer (EOC) is a significant cause of mortality among gynecological cancers. While Olaparib, a PARP inhibitor, has demonstrated efficacy in EOC maintenance therapy, individual responses vary. This study aims to assess the prognostic significance of body composition and systemic inflammation markers in EOC patients undergoing initial Olaparib treatment.

Music-evoked emotions classification using vision transformer in EEG signals.

Introduction: The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty.

The effect of music training on students' mathematics and physics development at middle schools in China: A longitudinal study.

As a descriptive-inferential study, this research aimed at revealing the relationship between music training and academic development with the Chinese middle school students' academic performance of mathematics and physics skills. The participants of this study consisted of the students from two different middle schools located at two cities in Shandong province, China. From each school 250 students were selected, and the statistics was used to analyze both the academic performance of the students and the data obtained from the scale designed by the authors.

Multi-label classification of retinal disease via a novel vision transformer model.

Introduction: The precise identification of retinal disorders is of utmost importance in the prevention of both temporary and permanent visual impairment. Prior research has yielded encouraging results in the classification of retinal images pertaining to a specific retinal condition. In clinical practice, it is not uncommon for a single patient to present with multiple retinal disorders concurrently.

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.

Background: Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning.

Purpose: This study aimed to develop a sophisticated deep learning framework called "Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolutional Neural Network (PCMM-Net)" to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM-Net should enhance the precision of LVI assessment.

Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer.

Rationale And Objectives: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions.

Materials And Methods: Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI.

Comparison of preoperative CT- and MRI-based multiparametric radiomics in the prediction of lymph node metastasis in rectal cancer.

Objective: To compare computed tomography (CT)- and magnetic resonance imaging (MRI)-based multiparametric radiomics models and validate a multi-modality, multiparametric clinical-radiomics nomogram for individual preoperative prediction of lymph node metastasis (LNM) in rectal cancer (RC) patients.

Methods: 234 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 164) and testing (n = 70) cohorts. The radiomics features of the primary tumor were extracted from the non-contrast enhanced computed tomography (NCE-CT), the enhanced computed tomography (CE-CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient.

Creating Order in Ultrastable Phosphonate Metal-Organic Frameworks via Isolable Hydrogen-Bonded Intermediates.

The stability presented by trivalent metal-organic frameworks (MOFs) makes them an attractive class of materials. With phosphonate-based ligands, crystallization is a challenge, as there are significantly more binding motifs that can be adopted due to the extra oxygen tether compared to carboxylate counterparts and the self-assembly processes are less reversible. Despite this, we have reported charge-assisted hydrogen-bonded metal-organic frameworks (HMOFs) consisting of [Cr(HO)] and phosphonate ligands, which were crystallographically characterized.

Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.

Background: To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC).

Methods: Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features.

Identification of emotions evoked by music via spatial-temporal transformer in multi-channel EEG signals.

Introduction: Emotion plays a vital role in understanding activities and associations. Due to being non-invasive, many experts have employed EEG signals as a reliable technique for emotion recognition. Identifying emotions from multi-channel EEG signals is evolving into a crucial task for diagnosing emotional disorders in neuroscience.