Unveiling major histocompatibility complex-mediated pan-cancer immune features by integrated single-cell and bulk RNA sequencing.

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, yet persistent challenges such as low response rate and significant heterogeneity necessitate attention. The pivotal role of the major histocompatibility complex (MHC) in ICI efficacy, its intricate impacts and potentials as a prognostic marker, warrants comprehensive exploration. This study integrates single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and spatial transcriptomic analyses to unveil pan-cancer immune characteristics governed by the MHC transcriptional feature (MHC.sig). Developed through scRNA-seq analysis of 663,760 cells across diverse cohorts and validated in 30 solid cancer types, the MHC.sig demonstrates a robust correlation between immune-related genes and infiltrating immune cells, highlighting its potential as a universal pan-cancer marker for anti-tumor immunity. Screening the MHC.sig for therapeutic targets using CRISPR data identifies potential genes for immune therapy synergy and validates its predictive efficacy for ICIs responsiveness across diverse datasets and cancer types. Finally, analysis of cellular communication patterns reveals interactions between C1QC+macrophages and malignant cells, providing insights into potential therapeutic agents and their sensitivity characteristics. This comprehensive analysis positions the MHC.sig as a promising marker for predicting immune therapy outcomes and guiding combinatorial therapeutic strategies.

Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.

BACKGROUND: Small intestinal vascular malformations (angiodysplasias) are common causes of small intestinal bleeding. While capsule endoscopy has become the primary diagnostic method for angiodysplasia, manual reading of the entire gastrointestinal tract is time-consuming and requires a heavy workload, which affects the accuracy of diagnosis. AIM: To evaluate whether artificial intelligence can assist the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time. METHODS: A convolutional neural network semantic segmentation model with a feature fusion method, which automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed. Resnet-50 was used as the skeleton network to design the fusion mechanism, fuse the shallow and depth features, and classify the images at the pixel level to achieve the segmentation and recognition of vascular dysplasia. The training set and test set were constructed and compared with PSPNet, Deeplab3+, and UperNet. RESULTS: The test set constructed in the study achieved satisfactory results, where pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time length to segment and recognize a picture was 0.6 s. CONCLUSION: Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.

Saccharomyces cerevisiae may serve as a probiotic in colorectal cancer by promoting cancer cell apoptosis.

OBJECTIVES: Shotgun metagenomic sequencing of human fecal samples has shown that Saccharomyces cerevisiae (S. cerevisiae) is significantly suppressed in colorectal cancer (CRC) and probably plays an important role in CRC progression. However, these results need to be validated. Here we aimed to confirm the results of high-throughput sequencing and demonstrate the mechanisms mediating the effect of S. cerevisiae on progression from colorectal adenoma (CRA) to CRC. METHODS: We used a quantitative polymerase chain reaction (qPCR) assay to examine the relative abundance of S. cerevisiae in 281 fecal samples collected from 106 healthy controls, 108 patients with CRA and 67 with CRC. C57BL/6 and APCMin/+ mouse models and in vitro cell assays were subsequntly used for additional analyses. The mouse models were treated or not treated with broad-spectrum antibiotics and given an S. cerevisiae gavage for 8 weeks. Western blot, 16S rRNA sequencing, qPCR, immunohistochemistry, RNA sequencing, cell counting kit-8 assay, colony formation assay and flow cytometry were performed. RESULTS: S. cerevisiae was 2.68-fold and 3.94-fold less abundant in patients with CRA and CRC, respectively, than in the controls. In vivo experiments showed that S. cerevisiae reduced colorectal tumor progression by promoting epithelial cell apoptosis and modulated gut microbial structure and intestinal immunity. S. cerevisiae downregulated nuclear factor kappa light chain enhancer of activated B cells and the mechanistic target of rapamycin signaling pathways. Cell assays confirmed the pro-apoptotic effect of S. cerevisiae. CONCLUSIONS: S. cerevisiae may play a probiotic role in CRC by promoting cancer cell apoptosis. It can reduce CRC progression by modulating the mucosal microbial structure.

[Applied study on support vector machine (SVM) regression method in quantitative analysis with near-infrared spectroscopy].

This paper introduced the application of support vector machines(SVM) regression method based on statistics studytheory to the quantitative analysis with near-infrared (NIR) spectroscopy. Sixty-six wheat samples were used as experimental materials, and thirty-three of them were used as calibration samples. The protein contents and NIR spectra of the calibration samples were used to build SVM regression models by four different kernel functions. The protein content of the predicting samples are estimated by four different SVM regression models. All of the correlation coefficients between the estimated values by different SVM regression models and the standard chemical values of protein content by Kjeldahl's method are more than 0.97. The average absolute error is less than 0.32. To investigate the predicting effect, it is compared with PLS regression models. The result suggested that the SVM regression, which was built to estimate the protein content of wheat samples, can also be used in the quantitative analysis of real samples by NIR.