Preclinical evaluation of antitumor activity and toxicity of TROP2-specific CAR-T cells for treatment of triple-negative breast cancer.

Background: Triple-negative breast cancer (TNBC) represents a subtype of breast cancer with poorest prognosis due to limited effective targeted therapies. Chimeric antigen receptor T cell (CAR-T) therapy has shown remarkable efficacy in treating hematological cancers, but its application in TNBC requires further development. One major obstacle is the lack of suitable tumor-specific target in TNBC.

Primary pulmonary spindle cell sarcoma with novel MBNL2::NUTM1 fusion and associated langerhans cell hyperplasia.

NUTM1-rearranged sarcomas are rare, genetically diverse tumors with variable histologic features and fusion partners. Their diagnosis remains challenging, especially in low-grade or unusual presentations. We describe the first reported case of a pulmonary spindle cell sarcoma with MBNL2::NUTM1 fusion in a 67-year-old man.

Feature recognition in multiple CNNs using sEMG images from a prototype comfort test.

Objective: Deep learning-based CNN networks have recently been investigated to solve the problem of body posture recognition based on surface electromyographic signals (sEMG). Influenced by these studies, to develop a combined approach of sEMG and CNNs in the study of human-product interactions and the impact of body comfort, and to compare the advantages and disadvantages of various CNNs networks.

Methods: In this study, sEMG measurements were carried out by building a prototype usability experiment, and the data were divided into four categories, with two types of datasets: training and testing.

Evaluation of Tumor Fibrosis in Pancreatic Ductal Adenocarcinoma by 2-D Shear Wave Elastography: A Pilot Study.

Objective: The aim of the work described here was to investigate the association of the stromal proportion with the elasticity obtained by 2-D shear wave elastography (SWE) and the diagnostic value of elasticity in evaluating tumor stromal fibrosis in pancreatic ductal adenocarcinoma (PDAC).

Methods: Patients who met inclusion criteria underwent pre-operative 2-D SWE examination and intra-operative determination of hardness by palpation from July 2021 to November 2022, and the post-operative specimens were used to evaluate pathological features including the tumor stromal proportion. A receiver operating characteristic curve was created to evaluate its diagnostic value in differentiating the degree of tumor stromal fibrosis.

ResNet and its application to medical image processing: Research progress and challenges.

Background And Objective: Deep learning, a novel approach and subset of machine learning, has drawn a growing amount of attention from computer vision researchers in recent years. This method has drawn a lot of interest because of its extraordinary ability to interpret medical pictures, especially when combined with residual neural networks, which have helped to progress the field.

Methods: In this paper, the following research is carried out on the residual network.

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI.

Background And Objective: Traditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy.

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart.

Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases.

Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network.

Objective: The traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value.

Investigation of the bio-stimulation of children's reading interest by chromatic pattern visual evoked potential.

Purpose: The characteristics of children's reading interest, stimulated by the visual evoked potentials of colour graphics in children's picture books, were tested to explore their normal reference value. The characteristics of chromatic pattern visual evoked potential (CP-VEP) can be harnessed by our methodology and may be applied to the visual screening of children in clinical ophthalmology.

Methods: The PR-650 spectral colour metre can strictly control factors, such as brightness and colour retention, based on colour contrast.

Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade.

Purpose: To evaluate the performance of machine learning (ML)-based computed tomography (CT) radiomics analysis for discriminating between low grade (WHO/ISUP I-II) and high grade (WHO/ISUP III-IV) clear cell renal cell carcinomas (ccRCCs).

Methods: A total of 164 low grade and 107 high grade ccRCCs were retrospectively analyzed in this study. Radiomic features were extracted from corticomedullary phase (CMP) and nephrographic phase (NP) CT images.

Mutational profiling of poorly differentiated and anaplastic thyroid carcinoma by the use of targeted next-generation sequencing.

Aims: To characterise the mutational profiles of poorly differentiated thyroid carcinoma (PDTC) and anaplastic thyroid carcinoma (ATC) and to identify markers with potential diagnostic, prognostic and therapeutic significance.

Methods And Results: Targeted next-generation sequencing with a panel of 18 thyroid carcinoma-related genes was performed on tissue samples from 41 PDTC and 25 ATC patients. Genetic alterations and their correlations with clinicopathological factors, including survival outcomes, were also analysed.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.

Objectives: To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features.

Methods: 161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients.

Induction chemotherapy followed by concurrent chemoradiotherapy is benefit for advanced stage nasopharyngeal carcinoma with different nonkeratinizing carcinoma subtypes.

Given the potentially distinctive histological variations in northwest of China, the aim of current study was to compare the efficacy of induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT) with concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients with different histological types. A total of 301 patients were included in this study. Patients were classified in two cohorts according to the 2005 WHO World Health Organization histological classification: WHO type IIa group and WHO type IIb group.