Lectin microarray based glycan profiling of exosomes for dynamic monitoring of colorectal cancer progression.

BACKGROUND: Exosomes, as emerging biomarkers in liquid biopsies in recent years, offer profound insights into cancer diagnostics due to their unique molecular signatures. The glycosylation profiles of exosomes have emerged as potential biomarkers, offering a novel and less invasive method for cancer diagnosis and monitoring. Colorectal cancer (CRC) represents a substantial global health challenge and burden. Thus there is a great need for the aberrant glycosylation patterns on the surface of CRC cell-derived exosomes, proposing them as potential biomarkers for tumor characterization. RESULTS: The interactions of 27 lectins with exosomes from three CRC cell lines (SW480, SW620, HCT116) and one normal colon epithelial cell line (NCM460) have been analyzed by the lectin microarray. The result indicates that Ulex Europaeus Agglutinin I (UEA-I) exhibits high affinity and specificity towards exosomes derived from SW480 cells. The expression of glycosylation related genes within cells has been analyzed by high-throughput quantitative polymerase chain reaction (HT-qPCR). The experimental result of HT-qPCR is consistent with that of lectin microarray. Moreover, the limit of detection (LOD) of UEA-I microarray is calculated to be as low as 2.7 × 105 extracellular vehicles (EVs) mL-1 (three times standard deviation (3σ) of blank sample). The UEA-I microarray has been successfully utilized to dynamically monitor the progression of tumors in mice-bearing SW480 CRC subtype, applicable in tumor sizes ranging from 2 mm to 20 mm in diameter. SIGNIFICANCE: The results reveal that glycan expression pattern of exosome is linked to specific CRC subtypes, and regulated by glycosyltransferase and glycosidase genes of mother cells. Our findings illuminate the potential of glycosylation molecules on the surface of exosomes as reliable biomarkers for diagnosis of tumor at early stage and monitoring of cancer progression.

Research on the bearing creep characteristics and constitutive model of gangue filling body.

The creep characteristics and potential deformation patterns of gangue backfill material are crucial in backfill mining operations. This study utilizes crushed gangue from the Gangue Yard in Fuxin City as the research material. An in-house designed, large-scale, triaxial gangue compaction test system was used. Triaxial compaction creep tests were conducted on gangue materials with varying particle size distributions. Analysis was performed based on different particle sizes, stresses, and confinement pressures. The study investigates the creep characteristics of the gangue under different conditions and explores the underlying causes. It reveals the relationship between the creep deformation of gangue materials and the passage of time. Mathematical methods are applied to develop a triaxial compaction creep power law model for gangue backfill materials. Finally, the creep results are fitted using an empirical formula approach.

Incorporating ions during thermal processing tailors the microstructure and practical features of rice starch/anthocyanin binary system.

Understanding the interplay among salt ions, anthocyanin and starch within food matrices under thermal conditions is important for the development of starch-based foods with demanded quality attributes. However, how salt ions presence influences the microstructure and properties of starch/anthocyanin binary system remains largely unclear. Herein, indica rice starch (IRS) and rice anthocyanin (RA) were used to construct an IRS-RA binary system, with thermal treatment under different concentrations of Na+ (10-40 mM) and types of salt ions (Na+ and Ca2+). The incorporation of salt ions induced the formation of a porous gel matrix, and destroyed the hydrogen bond between starch and anthocyanin through electrostatic interactions, reducing the storage modulus and radius of gyration of the binary system, and increasing the relative crystallinity (from 1.08 % to 1.51 % (20 mM Na+) and 1.69 % (20 mM Ca+)) of the IRS-RA binary system at 90 °C. Also, the DPPH radical scavenging ability of the binary system at 90 °C was enhanced upon incorporating salt ions (0.93 for Na+ condition and 0.94 for Ca2+ condition at 20 mM ion concentration). It is noteworthy that Ca2+ inclusion had more significant effects than the case for Na+ presence, presumably due to the increased charge density.

Integration analysis of transcriptome and proteome profiles brings new insights of somatic embryogenesis of two eucalyptus species.

BACKGROUND: Somatic embryogenesis (SE) is recognized as a promising technology for plant vegetative propagation. Although previous studies have identified some key regulators involved in the SE process in plant, our knowledge about the molecular changes in the SE process and key regulators associated with high embryogenic potential is still poor, especially in the important fiber and energy source tree - eucalyptus. RESULTS: In this study, we analyzed the transcriptome and proteome profiles of E. camaldulensis (with high embryogenic potential) and E. grandis x urophylla (with low embryogenic potential) in SE process: callus induction and development. A total of 12,121 differentially expressed genes (DEGs) and 3,922 differentially expressed proteins (DEPs) were identified in the SE of the two eucalyptus species. Integration analysis identified 1,353 (131 to 546) DEGs/DEPs shared by the two eucalyptus species in the SE process, including 142, 13 and 186 DEGs/DEPs commonly upregulated in the callus induction, maturation and development, respectively. Further, we found that the trihelix transcription factor ASR3 isoform X2 was commonly upregulated in the callus induction of the two eucalyptus species. The SOX30 and WRKY40 TFs were specifically upregulated in the callus induction of E. camaldulensis. Three TFs (bHLH62, bHLH35 isoform X2, RAP2-1) were specifically downregulated in the callus induction of E. grandis x urophylla. WGCNA identified 125 and 26 genes/proteins with high correlation (Pearson correlation > 0.8 or < -0.8) with ASR3 TF in the SE of E. camaldulensis and E. grandis x urophylla, respectively. The potential target gene expression patterns of ASR3 TF were then validated using qRT-PCR in the material. CONCLUSIONS: This is the first time to integrate multiple omics technologies to study the SE of eucalyptus. The findings will enhance our understanding of molecular regulation mechanisms of SE in eucalyptus. The output will also benefit the eucalyptus breeding program.

Spatiotemporal Analysis of HIV/AIDS Incidence in China From 2009 to 2019 and Its Association With Socioeconomic Factors: Geospatial Study.

BACKGROUND: The Joint United Nations Program on HIV/AIDS (UNAIDS) has set the "95-95-95" targets to ensure that 95% of all people living with HIV will know their HIV status, 95% of all people living with HIV will receive sustained antiretroviral therapy (ART), and 95% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across China and its association with socioeconomic factors. OBJECTIVE: This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development. METHODS: This study employed ArcGIS 10.2 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence in China. RESULTS: The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016. An analysis of the impact of socioeconomic factors on HIV/AIDS incidence in China highlighted the "urbanization rate" and "urban basic medical insurance fund expenditure" as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence. CONCLUSIONS: From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.

A Multimodal Generative AI Copilot for Human Pathology.

The field of computational pathology[1,2] has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders[3,4]. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants and copilots[5] tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We build PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and finetuning the whole system on over 456,000 diverse visual language instructions consisting of 999,202 question-answer turns. We compare PathChat against several multimodal vision language AI assistants and GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4[7]. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases of diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision-language AI Copilot that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

[Box: see text].

Association between serum chloride and in-hospital mortality in congestive heart failure with diabetes: Data from the MIMIC-IV database.

Background: Congestive heart failure (CHF) demonstrates a heightened prevalence in individuals with diabetes mellitus within Intensive Care Units. The occurrence of abnormal chloride levels is frequently observed in critically ill patients, yet its clinical significance remains subject to debate. This study endeavors to explore the relationship between serum chloride levels and in-hospital mortality among patients affected by both congestive heart failure and diabetes. Methods: A retrospective cohort study was conducted, utilizing data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, focusing on adult patients in the United States. The impact of serum chloride levels upon ICU admission on in-hospital mortality was analyzed using multivariable logistic regression models, generalized additive models and subgroup analysis. Results: The study encompassed 7,063 patients with coexisting diabetes and congestive heart failure. The fully adjusted model revealed an inverse association between serum chloride levels and in-hospital mortality. As a tertile variable (Q3 vs Q1), the odds ratio (OR) was 0.73 with a 95% confidence interval (CI) of 0.54-0.98 (p = 0.039). As a continuous variable, per 1 mmol/L increment, the OR (95% CI) was 0.97 (0.96-0.99, p = 0.01). The relationship between serum chloride and in-hospital mortality demonstrated linearity (non-linear p = 0.958). Stratified analyses further validated the robustness of this correlation. Conclusions: Serum chloride levels exhibited a negative association with in-hospital mortality in patients with both congestive heart failure and diabetes. Nevertheless, prospective, randomized, controlled studies are warranted to corroborate and validate the findings presented in this investigation.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Ultra-fast fabrication of MXene/PVA composite films through glutaraldehyde induced microgel framework.

In this study, Ti3C2Tx/PVA microgels were assembled through the introduction of glutaraldehyde and PVA into Ti3C2Tx colloids. Subsequently, the microgels underwent vacuum-assisted filtration (VAF) and drying processes to fabricate Ti3C2Tx/PVA self-assembled films (MPGF). This research effectively reduced VAF time by introducing a small amount of glutaraldehyde. The findings demonstrate that glutaraldehyde's chemical crosslinking prompts the formation of temporary microgel frameworks between Ti3C2Tx and PVA, enhancing water molecule transfer during VAF and improving film formation efficiency. Further analysis links VAF time is related to the particle size distribution of the microgels. Adjusting crosslinking and PVA quantity alters microgel crystalline structure and -OH hydrogen bonds, affecting particle size and VAF time. Additionally, films produced via rapid VAF exhibit promising mechanical properties for practical applications.

Research on defect detection of bottle cap interior based on low-angle and large divergence angle vision system.

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements.

Genome-wide analysis of UDP-glycosyltransferases family and identification of UGT genes involved in drought stress of Platycodon grandiflorus.

Introduction: The uridine diphosphate (UDP)-glycosyltransferase (UGT) family is the largest glycosyltransferase family, which is involved in the biosynthesis of natural plant products and response to abiotic stress. UGT has been studied in many medicinal plants, but there are few reports on Platycodon grandiflorus. This study is devoted to genome-wide analysis of UGT family and identification of UGT genes involved in drought stress of Platycodon grandiflorus (PgUGTs). Methods: The genome data of Platycodon grandiflorus was used for genome-wide identification of PgUGTs, online website and bioinformatics analysis software was used to conduct bioinformatics analysis of PgUGT genes and the genes highly responsive to drought stress were screened out by qRT-PCR, these genes were cloned and conducted bioinformatics analysis. Results: A total of 75 PgUGT genes were identified in P.grandiflorus genome and clustered into 14 subgroups. The PgUGTs were distributed on nine chromosomes, containing multiple cis-acting elements and 22 pairs of duplicate genes were identified. Protein-protein interaction analysis was performed to predict the interaction between PgUGT proteins. Additionally, six genes were upregulated after 3d under drought stress and three genes (PGrchr09G0563, PGrchr06G0523, PGrchr06G1266) responded significantly to drought stress, as confirmed by qRT-PCR. This was especially true for PGrchr06G1266, the expression of which increased 16.21-fold after 3d of treatment. We cloned and conducted bioinformatics analysis of three candidate genes, both of which contained conserved motifs and several cis-acting elements related to stress response, PGrchr06G1266 contained the most elements. Discussion: PgGT1 was confirmed to catalyze the C-3 position of platycodin D and only eight amino acids showed differences between gene PGr008G1527 and PgGT1, which means PGr008G1527 may be able to catalyze the C-3 position of platycodin D in the same manner as PgGT1. Seven genes were highly expressed in the roots, stems, and leaves, these genes may play important roles in the development of the roots, stems, and leaves of P. grandiflorus. Three genes were highly responsive to drought stress, among which the expression of PGrchr06G1266 was increased 16.21-fold after 3d of drought stress treatment, indicating that PGrchr06G1266 plays an important role in drought stress tolerance. To summarize, this study laied the foundation to better understand the molecular bases of responses to drought stress and the biosynthesis of platycodin.

Dilute RuCo Alloy Synergizing Single Ru and Co Atoms as Efficient and CO-Resistant Anode Catalyst for Anion Exchange Membrane Fuel Cells.

Ruthenium (Ru) is considered a promising candidate catalyst for alkaline hydroxide oxidation reaction (HOR) due to its hydrogen binding energy (HBE) like that of platinum (Pt) and its much higher oxygenophilicity than that of Pt. However, Ru still suffers from insufficient intrinsic activity and CO resistance, which hinders its widespread use in anion exchange membrane fuel cells (AEMFCs). Here, we report a hybrid catalyst (RuCo)NC+SAs/N-CNT consisting of dilute RuCo alloy nanoparticles and atomically single Ru and Co atoms on N-doped carbon nanotubes The catalyst exhibits a state-of-the-art activity with a high mass activity of 7.35 A mgRu -1. More importantly, when (RuCo)NC+SAs/N-CNT is used as an anode catalyst for AEMFCs, its peak power density reaches 1.98 W cm-2, which is one of the best AEMFCs properties of noble metal-based catalysts at present. Moreover, (RuCo)NC+SAs/N-CNT has superior long-time stability and CO resistance. The experimental and density functional theory (DFT) results demonstrate that the dilute alloying and monodecentralization of the exotic element Co greatly modulates the electronic structure of the host element Ru, thus optimizing the adsorption of H and OH and promoting the oxidation of CO on the catalyst surface, and then stimulates alkaline HOR activity and CO tolerance of the catalyst.

Computational Interpersonal Communication Model for Screening Autistic Toddlers: A Case Study of Response-to-Name.

Interpersonal communication facilitates symptom measures of autistic sociability to enhance clinical decision-making in identifying children with autism spectrum disorder (ASD). Traditional methods are carried out by clinical practitioners with assessment scales, which are subjective to quantify. Recent studies employ engineering technologies to analyze children's behaviors with quantitative indicators, but these methods only generate specific rule-driven indicators that are not adaptable to diverse interaction scenarios. To tackle this issue, we propose a Computational Interpersonal Communication Model (CICM) based on psychological theory to represent dyadic interpersonal communication as a stochastic process, providing a scenario-independent theoretical framework for evaluating autistic sociability. We apply CICM to the response-to-name (RTN) with 48 subjects, including 30 toddlers with ASD and 18 typically developing (TD), and design a joint state transition matrix as quantitative indicators. Paired with machine learning, our proposed CICM-driven indicators achieve consistencies of 98.44% and 83.33% with RTN expert ratings and ASD diagnosis, respectively. Beyond outstanding screening results, we also reveal the interpretability between CICM-driven indicators and expert ratings based on statistical analysis.

REMI: Few-Shot ISAR Target Classification via Robust Embedding and Manifold Inference.

Unknown image deformation and few-shot issues have posed significant challenges to inverse synthetic aperture radar (ISAR) target classification. To achieve robust feature representation and precise correlation modeling, this article proposes a novel two-stage few-shot ISAR classification network, dubbed as robust embedding and manifold inference (REMI). In the robust embedding stage, a multihead spatial transformation network (MH-STN) is designed to adjust unknown image deformations from multiple perspectives. Then, the grouped embedding network (GEN) integrates and compresses diverse information by grouped feature extraction, intermediate feature fusion, and global feature embedding. In the manifold inference stage, a masked Gaussian graph attention network (MG-GAT) is devised to capture the irregular manifold of samples in the embedding space. In particular, the node features are described by Gaussian distributions, with interactions guided by the masked attention mechanism. Experimental results on two ISAR datasets demonstrate that REMI significantly improves the performance of few-shot classification and exhibits robustness in various scenarios.

Association between dietary folate intake and severe abdominal aorta calcification in adults: A cross-sectional analysis of the national health and nutrition examination survey.

BACKGROUND: Prior studies have established a connection between folate intake and cardiovascular disease (CVD). Abdominal aortic calcification (AAC) has been introduced as a good predictor of CVD events, but no previous study has investigated the relationship between dietary folate intake and severe AAC. Therefore, the study aims to explore the association between dietary folate intake and severe AAC in the United States (US) middle-aged and elderly population. METHODS: This study employed cross-sectional data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) to examine the relationship between dietary folate intake and severe AAC. Two 24-h dietary recall interviews were conducted to assess dietary folate intake and its sources, while a DXA scan was used to determine the AAC score. To analyze the association between dietary folate intake and severe AAC, a multivariable logistic regression model was applied, and a subgroup analysis was performed. RESULTS: Our analysis utilized data from 2640 participants aged 40 years and above, including 288 individuals diagnosed with severe AAC. After adjusting for confounding factors, we observed an inverted L-shaped association between folate intake and severe AAC. Upon further adjustment for specific confounding factors and covariates, the multivariable-adjusted odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the second, third, and fourth quartiles of folate intake, using the first quartile as the reference, were as follows: 1.24 (0.86-1.79), 0.86 (0.58-1.27), and 0.63 (0.41-0.97), respectively. Subgroup analysis results were consistent with the logistic regression models, indicating concordant findings. Moreover, no significant interaction was observed in the subgroup analyses. CONCLUSIONS: The study findings suggest an inverted L-shaped association between dietary folate intake and severe AAC. However, additional prospective investigations are necessary to explore the impact of dietary folate intake on severe AAC in patients.

Diverse mode operation fiber laser mode-locked by nonlinear multimode interference.

We present an all-fiber passively mode-locked (ML) laser with a nonlinear multimode interference (NLMI)-based saturable absorber (SA) capable of generating five pulse modes. The SA consists of two centrally aligned graded index multimode fiber (GIMF) with different diameters (105-50 µm) and features a widely adjustable transmission with saturable/reverse-saturable absorption. Based on this, dissipative soliton (DS), Q-switched rectangular pulse (QRP), dissipative soliton resonance (DSR), noise-like pulse (NLP) and bright-dark pulse pairs (BDP) are observed at three dispersions without additional filter. The DS has a pulse energy, bandwidth and duration of up to 1.15 nJ, 17.98 nm and ∼2.78 ps. The achievable pulse duration and energy of DSR and NLP are 5.21, 48.06 ns and 4.53, 5.12 nJ, respectively. Furthermore, it is demonstrated that the BDP is superimposed by a chair-case pulse (CP) and a rectangular pulse (RP) belonging to orthogonal polarization states. The versatility, flexibility, simplicity and energy scalability of the large-core hybrid GIMF-SA, make it interesting and highly attractive in ultrafast photonics.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Genetic variants in m5C modification genes are associated with survival of patients with HBV-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The 5-methylcytosine (m5C), a type of RNA modification, plays crucial regulatory roles in HCC carcinogenesis, metastasis, and prognosis. However, a few studies have investigated the effect of genetic variants in m5C modification genes on survival of patients with hepatitis B virus (HBV)-related HCC. In the present study, we evaluated associations between 144 SNPs in 15 m5C modification genes and overall survival (OS) in 866 patients with the HBV-related HCC. Expression quantitative trait loci (eQTL) analysis and differential expression analysis were conducted to investigate biological mechanisms. As a result, we identified that two SNPs (NSUN7 rs2437325 A > G and TRDMT1 rs34434809 G > C) were significantly associated with HBV-related HCC OS with adjusted allelic hazards ratios of 1.25 (95% confidence interval = 1.05-1.48 and P = 0.011) and 1.19 (1.02-1.38 and P = 0.027), respectively, with a trend of combined risk genotypes (Ptrend < 0.001). Moreover, the results of eQTL analyses showed that both NSUN7 rs2437325 G and TRDMT1 rs34434809 C alleles were associated with a reduced mRNA expression level in 208 normal liver tissues (P = 0.007 and P < 0.001, respectively). Taken together, genetic variants in the m5C modification genes may be potential prognostic biomarkers of HBV-related HCC after hepatectomy, likely through mediating the mRNA expression of corresponding genes.