Macrophage-coated tumor cluster aggravates hepatoma invasion and immunotherapy resistance via generating local immune deprivation.

Immune checkpoint inhibitors (ICIs) represent a promising treatment for hepatocellular carcinoma (HCC) due to their capacity for abundant lymphocyte infiltration. However, some patients with HCC respond poorly to ICI therapy due to the presence of various immunosuppressive factors in the tumor microenvironment. Our research reveals that a macrophage-coated tumor cluster (MCTC) signifies a unique spatial structural organization in HCC correlating with diminished recurrence-free survival and overall survival in a total of 572 HCC cases from 3 internal cohorts and 2 independent external validation cohorts. Mechanistically, tumor-derived macrophage-associated lectin Mac-2 binding protein (M2BP) induces MCTC formation and traps immunocompetent cells at the edge of MCTCs to induce intratumoral cytotoxic T cell exclusion and local immune deprivation. Blocking M2BP with a Mac-2 antagonist might provide an effective approach to prevent MCTC formation, enhance T cell infiltration, and thereby improve the efficacy of ICI therapy in HCC.

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.

Gas migration characteristics in vehicle tank under different ventilation conditions.

In recent years, due to the frequent occurrence of accidents in confined space operations, horizontal ammonia tank trucks with higher accident frequencies were selected for numerical simulation research through comparative analysis. The ammonia concentration variation characteristics of horizontal ammonia tank cars were simulated under four conditions: natural ventilation with 0° incoming air, natural ventilation with 45° incoming air, mechanical ventilation with extraction, and mechanical ventilation with compression. The results indicate that natural ventilation requires 48 h to reduce the ammonia concentration to a safe range for operation, while mechanical ventilation reduces the ammonia concentration to infinity and approaches zero within 30 min according to regulations, making the working environment safer; Set up monitoring points inside the tank to monitor the gas disturbance inside the tank at different wind speeds. Based on the ammonia concentration cloud map and the monitoring point wind speed, it can be concluded that local ammonia accumulation is more likely to occur on both sides of the tank due to poor ventilation. Comparing and analyzing the simulated values with theoretical calculations and experiments, it was found that there are differences in the degree of gas change but the overall trend is the same. This indicates that ventilation simulation and the determination of ammonia migration characteristics have practical significance for guiding on-site operations.

Language model enables end-to-end accurate detection of cancer from cell-free DNA.

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

An efficient context-aware approach for whole-slide image classification.

Computational pathology for gigapixel whole-slide images (WSIs) at slide level is helpful in disease diagnosis and remains challenging. We propose a context-aware approach termed WSI inspection via transformer (WIT) for slide-level classification via holistically modeling dependencies among patches on WSI. WIT automatically learns feature representation of WSI by aggregating features of all image patches. We evaluate classification performance of WIT and state-of-the-art baseline method. WIT achieved an accuracy of 82.1% (95% CI, 80.7%-83.3%) in the detection of 32 cancer types on the TCGA dataset, 0.918 (0.910-0.925) in diagnosis of cancer on the CPTAC dataset, and 0.882 (0.87-0.890) in the diagnosis of prostate cancer from needle biopsy slide, outperforming the baseline by 31.6%, 5.4%, and 9.3%, respectively. WIT can pinpoint the WSI regions that are most influential for its decision. WIT represents a new paradigm for computational pathology, facilitating the development of digital pathology tools.

Single-Component Color-Tunable Smart Organic Emitters with Simultaneous Multistage Stimuli-Responsiveness and Multimode Emissions.

Achieving color-tunable emission in single-component organic emitters with multistage stimuli-responsiveness is of vital significance for intelligent optoelectronic applications, but remains enormously challenging. Herein, we present an unprecedented example of a color-tunable single-component smart organic emitter (DDOP) that simultaneously exhibits multistage stimuli-responsiveness and multimode emissions. DDOP based on a highly twisted amide-bridged donor-acceptor-donor structure has been found to facilitate intersystem crossing, form multimode emissions, and generate multiple emissive species with multistage stimuli-responsiveness. DDOP pristine crystalline powders exhibit abnormal excitation-dependent emissions from a monomer-dominated blue emission centered at 470 nm to a dimer-dominated yellow emission centered at 550 nm through decreasing the ultraviolet (UV) excitation wavelengths, whereas DDOP single crystals show a wide emission band with a main emission peak at 585 nm when excited at different wavelengths. The emission behaviors of pristine crystalline powders and single crystals are different, demonstrating emission features that are closely related to the aggregation states. The work has developed color-tunable single-component organic emitters with simultaneous multistage stimuli-responsiveness and multimode emissions, which is vital for expanding intelligent optoelectronic applications, including multilevel information encryption, multicolor emissive patterns, and visual monitoring of UV wavelengths.

Research on psychophysiological characteristics of construction workers during consciously unsafe behaviors.

Workers' unsafe behavior is a primary cause leading to falling accidents on construction sites. This study aimed to explore how to utilize psychophysiological characteristics to predict consciously unsafe behaviors of construction workers. In this paper, a psychological questionnaire was compiled to measure risky psychology, and wireless wearable physiological recorders were employed to real-timely measure the physiological signals of subjects. The psychological and physiological characteristics were identified by correlation analysis and significance test, which were then utilized to develop unsafe behavior prediction models based on multiple linear regression and decision tree regressor. It was revealed that unsafe behavior performance was negatively correlated with task-related risk perception, while positively correlated with hazardous attitude. Subjects experienced remarkable increases in skin conductivity, while notable decreases in the inter-beat interval and skin temperature during consciously unsafe behavior. Both models developed for predicting unsafe behavior were reliably and well-fitted with coefficients of determination higher than 0.8. Whereas, each model exhibited its unique advantages in terms of prediction accuracy and interpretability. Not only could study results contribute to the body of knowledge on intrinsic mechanisms of unsafe behavior, but also provide a theoretical basis for the automatic identification of workers' unsafe behavior.

Comparative analysis of domestic and foreign coal mine safety supervision modes based on knowledge map.

In order to grasp the research status and hot frontier of coal mine safety supervision mode in the world in the past 40 years, this paper takes the relevant literature in the field of "coal mine safety" and "supervision" included in the core collection of Web of Science (WOS) and the core journals of CNKI as the data source; based on the methods of statistical analysis and bibliometrics, the visualization analysis software CiteSpace is used to draw the map of scientific knowledge. Through the visualization analysis of the main research institutions, countries, and authors in this field, the main research forces and the distribution of researchers in this field are described. Through the visualization analysis of key words and research clustering, the research hotspots and future development trends in this field are described. The results show that the research hotspots of coal mine safety supervision mode at home and abroad are roughly the same, and the researchers and institutions in this field still need to further carry out cross regional and cross departmental wide area cooperation.

Evolutionary Model and Experimental Validation of Gas-Bearing Coal Permeability under Negative Pressure Conditions.

Coal bed methane drainage is the main approach to lower risks of coal seam while raising the efficiency in natural resource utilization. The negative pressure used for extraction in coal mines is largely determined empirically due to a lack of experimental research on how coal permeability changes under the combined influence of effective stress and negative pressure. This results in low gas extraction efficiency and concentration. In this paper, to study the effect law of complex stress and extraction on coal permeability during coal and gas co-mining, a test system was specially designed to determine the gas flow and coal permeability of coal samples under different stress paths and negative pressure conditions in the lab. The study analyzed the correlation between coal permeability, effective stress, and negative pressure and subsequently developed a permeability evolution model for gas-bearing coal under negative pressure conditions. The results showed that the permeability of coal increases with the increase in negative pressure and decreases with the increase in effective stress; the permeability of coal can be abruptly changed by changes in stress loading patterns; the established model of permeability evolution of gas-bearing coal can better reflect the correlation between permeability, effective stress, and negative pressure. The research outcomes offer a valuable theoretical foundation for the efficient extraction and utilization of methane in coal mines.

Generative pretraining from large-scale transcriptomes for single-cell deciphering.

Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled generative pretraining from transcriptomes (tGPT) for learning feature representation of transcriptomes. tGPT is conceptually simple in that it autoregressive models the ranking of a gene in the context of its preceding neighbors. We developed tGPT with 22.3 million single-cell transcriptomes and used four single-cell datasets to evalutate its performance on single-cell analysis tasks. In addition, we examine its applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis, and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amounts of transcriptome data and it will facilitate the interpretation and clinical translation of single-cell transcriptomes.

Integrated analysis of racial disparities in genomic architecture identifies a trans-ancestry prognostic subtype in bladder cancer.

The incidence of bladder cancer and patient survival vary greatly among different populations, but the influence of the associated molecular features and evolutionary processes on its clinical treatment and prognostication remains unknown. Here, we analyze the genomic architectures of 505 bladder cancer patients from Asian/Black/White populations. We identify a previously unknown association between AHNAK mutations and activity of the APOBEC-a mutational signature, the activity of which varied substantially across populations. All significantly mutated genes but only half of arm-level somatic copy number alterations (SCNAs) are enriched with clonal events, indicating large-scale SCNAs as rich sources of bladder cancer clonal diversities. The prevalence of TP53 and ATM clonal mutations as well as the associated burden of SCNAs is significantly higher in Whites/Blacks than in Asians. We identify a trans-ancestry prognostic subtype of bladder cancer characterized by enrichment of non-muscle-invasive patients and muscle-invasive patients with good prognosis, increased CREBBP/FGFR3/HRAS/NFE2L2 mutations, decreased intra-tumor heterogeneity and genome instability, and an activated tumor microenvironment.

Intrinsically Stretchable Electroluminescent Elastomers with Self-Confinement Effect for Highly Efficient Non-Blended Stretchable OLEDs.

Ultra-flexible stretchable organic light-emitting diodes (OLEDs) are emerging as a basic component of flexible electronics and human-machine interfaces. However, the brightness and efficiency of stretchable OLEDs remain still far inferior to their rigid counterparts, owing to the scarcity of satisfactory stretchable electroluminescent materials. Herein, we explore a general concept based on the self-confinement effect to dramatically improve the stretchability of elastomers, without affecting electroluminescent properties. The balanced rigid/flexible chain dynamics under self-confinement significantly reduces the modulus of the elastomers, resulting in the maximum strain reaching 806 %. Ultra-flexible stretchable OLEDs have been constructed based on the resulting ISEEs, achieving unprecedented high-performance non-blended stretchable OLEDs. The results suggest an effective molecular design strategy for highly deformable stretchable displays and flexible electronics.

A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma.

Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

Human papillomavirus integration perspective in small cell cervical carcinoma.

Small cell cervical carcinoma (SCCC) is a rare but aggressive malignancy. Here, we report human papillomavirus features and genomic landscape in SCCC via high-throughput HPV captured sequencing, whole-genome sequencing, whole-transcriptome sequencing, and OncoScan microarrays. HPV18 infections and integrations are commonly detected. Besides MYC family genes (37.9%), we identify SOX (8.4%), NR4A (6.3%), ANKRD (7.4%), and CEA (3.2%) family genes as HPV-integrated hotspots. We construct the genomic local haplotype around HPV-integrated sites, and find tandem duplications and amplified HPV long control regions (LCR). We propose three prominent HPV integration patterns: duplicating oncogenes (MYCN, MYC, and NR4A2), forming fusions (FGFR3-TACC3 and ANKRD12-NDUFV2), and activating genes (MYC) via the cis-regulations of viral LCRs. Moreover, focal CNA amplification peaks harbor canonical cancer genes including the HPV-integrated hotspots within MYC family, SOX2, and others. Our findings may provide potential molecular criteria for the accurate diagnosis and efficacious therapies for this lethal disease.

The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance.

The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.

Scalable batch-correction approach for integrating large-scale single-cell transcriptomes.

Integration of accumulative large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose Fugue, a simple and efficient batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is to encode batch information as trainable parameters and add it to single-cell expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of Fugue by integrating all single cells obtained from the Human Cell Atlas. We benchmark Fugue against current state-of-the-art methods and show that Fugue consistently achieves improved performance in terms of data alignment and clustering preservation. Our study will facilitate the integration of single-cell transcriptomes at increasingly large scale.

Deep learning to diagnose Hashimoto's thyroiditis from sonographic images.

Hashimoto's thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836-0.939) and 0.895 (0.862-0.927). HTNet exceeds radiologists' performance on accuracy (83.2% versus 79.8%; binomial test, p < 0.001) and sensitivity (82.6% versus 68.1%; p < 0.001). By integrating serologic markers with imaging data, the performance of HTNet was significantly and marginally improved on the video (AUC, 0.949 versus 0.888; DeLong's test, p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.

A PARylation-phosphorylation cascade promotes TOPBP1 loading and RPA-RAD51 exchange in homologous recombination.

The efficiency of homologous recombination (HR) in the repair of DNA double-strand breaks (DSBs) is closely associated with genome stability and tumor response to chemotherapy. While many factors have been functionally characterized in HR, such as TOPBP1, their precise regulation remains unclear. Here, we report that TOPBP1 interacts with the RNA-binding protein HTATSF1 in a cell-cycle- and phosphorylation-dependent manner. Mechanistically, CK2 phosphorylates HTATSF1 to facilitate binding to TOPBP1, which promotes S-phase-specific TOPBP1 recruitment to damaged chromatin and subsequent RPA/RAD51-dependent HR, genome integrity, and cancer-cell viability. The localization of HTATSF1-TOPBP1 to DSBs is potentially independent of the transcription-coupled RNA-binding and processing capacity of HTATSF1 but rather relies on the recognition of poly(ADP-ribosyl)ated RPA by HTATSF1, which can be blunted with PARP inhibitors. Together, our study provides a mechanistic insight into TOPBP1 loading at HR-prone DSB sites via HTATSF1 and reveals how RPA-RAD51 exchange is tuned by a PARylation-phosphorylation cascade.

Integrated analysis of expression profiles with meat quality traits in cattle.

MicroRNAs (miRNAs) play a vital role in improving meat quality by binding to messenger RNAs (mRNAs). We performed an integrated analysis of miRNA and mRNA expression profiling between bulls and steers based on the differences in meat quality traits. Fat and fatty acids are the major phenotypic indices of meat quality traits to estimate between-group variance. In the present study, 90 differentially expressed mRNAs (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Eighty-three potential DEG targets and 18 DEMs were used to structure a negative interaction network, and 75 matching target genes were shown in this network. Twenty-six target genes were designated as intersection genes, screened from 18 DEMs, and overlapped with the DEGs. Seventeen of these genes enriched to 19 terms involved in lipid metabolism. Subsequently, 13 DEGs and nine DEMs were validated using quantitative real-time PCR, and seven critical genes were selected to explore the influence of fat and fatty acids through hub genes and predict functional association. A dual-luciferase reporter and Western blot assays confirmed a predicted miRNA target (bta-miR-409a and PLIN5). These findings provide substantial evidence for molecular genetic controls and interaction among genes in cattle.