Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. RESULTS: Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. AVAILABILITY AND IMPLEMENTATION: The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.

MiR-133a-3p Inhibits Oral Squamous Cell Carcinoma (OSCC) Proliferation and Invasion by Suppressing COL1A1.

The aim of our study was to investigate the effects of miR-133a-3p on human oral squamous cell carcinoma (OSCC) cells by regulating gene COL1A1. OSCC tissues, adjacent tongue epithelial tissues, the immortalized oral epithelial cell line HIOEC, and OSCC cell lines (CAL-27, TCA-8113, SCC-4, SCC-9, and SCC-15) were used in this research. Quantitative real-time PCR (RT-qPCR) was employed to determine the expression of miR-133a-3p and COL1A1. Dual luciferase reporter gene assay and Western blot were applied to verify the binding relationship between miR-133a-3p and COL1A1. Functional assays were also conducted in this study, including CCK-8 assay, colony formation assay, flow cytometry analysis as well as Transwell assay. MiR-133a-3p was found low-expressed both in OSCC tissues and cells lines compared with normal tissues and cell line, respectively, whereas COL1A1 was just the opposite. The over-expression of miR-133a-3p or the down-regulation of COL1A1 suppressed the proliferation, invasion, and mitosis of OSCC cells, whereas simultaneous down-regulation of miR-133a-3p and up-regulation of COL1A1 led to no significant alteration of cell activities. MiR-133a-3p could inhibit the proliferation and migration of OSCC cells through directly targeting COL1A1 and reducing its expression. J. Cell. Biochem. 119: 338-346, 2018. © 2017 Wiley Periodicals, Inc.

Surfactant-Mediated Crystallization-Driven Self-Assembly of Crystalline/Ionic Complexed Block Copolymers in Aqueous Solution.

A series of crystalline/ionic complexed block copolymers (BCPs) with various compositions have been prepared by sequential reactions. The BCPs with different hydrophilic fractions can self-assemble into various morphologies, such as spindlelike, rodlike, and spherical micelles with different crystallinity of the core. Bis(2-ethylhexyl) sulfosuccinate sodium salt (AOT) is added as a surfactant to induce the morphological transition of BCPs in aqueous media. The introduced AOT can be tightly bound to the cationic units, and a water-insoluble unit in the corona forms, leading to a reduced tethering density. Consequently, morphological variety changing from rods to platelets to fibril to dendrite-like micelles can be observed.

Design and synthesis of fluorinated dendrimers for sensitive (19)F MRI.

To achieve high sensitivity for (19)F MRI, a class of novel dendritic molecules with multiple pseudosymmetrical fluorines was designed and efficiently synthesized. Through iterative bromination and Williamson ether synthesis under mild conditions, a fluorinated dendrimer with 540 pseudosymmetrical fluorines was conveniently prepared without performing the group protection in a convergent way. The dendrimer is characterized by a strong (19)F NMR peak and short relaxation times. Eventually, an appreciably enhanced (19)F MRI at an extremely low concentration (18.5 µM) was achieved, which demonstrated the potential utility of such dendritic molecules in highly sensitive (19)F MRI.

Design and Synthesis of Fluorinated Amphiphile as (19)F MRI/Fluorescence Dual-Imaging Agent by Tuning the Self-Assembly.

Both (19)F MRI and optical imaging are powerful noninvasive molecular imaging modalities in biomedical applications. To integrate these two complementary imaging modalities, the design and synthesis of a novel (19)F MRI/fluorescence dual-modal imaging agent is reported herein. Through Sonogashira coupling reaction between the fluorinated phenylacetylene and 1,2,4,5-tetraiodobenzene, a fluorophore with 48 symmetrical fluorines at its periphery was constructed with high efficacy. High aqueous solubility was achieved by PEGylation of the fluorophore with monodisperse PEGs. However, an unexpected self-assembly of the PEGylated amphiphilic fluorophore in water "turned off" the (19)F NMR signal. However, hydrogenation of the triple bonds or introduction of branched monodisperse PEGs was able to efficiently tune the self-assembly, resulting in the "turning on" of the (19)F NMR signal. One of these amphiphiles combines the advantages of label-free fluorescence, high (19)F MRI sensitivity, biocompatibility, and excellent aqueous solubility. The results demonstrate the great potential of such amphiphiles for real-time (19)F MRI and fluorescence dual-modality imaging.

Evaluating cardiac disorders associated with triazole antifungal agents based on the US Food and Drug Administration Adverse Event reporting system database.

Introduction: Triazole antifungal agents are widely used to treat and prevent systemic mycoses. With wide clinical use, the number of reported adverse events has gradually increased. The aim of this study was to analyze the cardiac disorders associated with TAAs (fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole) based on data from the US Food and Drug Administration Adverse Event Reporting System FDA Adverse Event Reporting System. Methods: Data were extracted from the FAERS database between the first quarter of 2004 and third quarter of 2022. The clinical characteristics in TAA-associated cardiac AE reports were analyzed. Disproportionality analysis was performed to evaluate the potential association between AEs and TAAs using the reporting odds ratio (ROR) and proportional reporting ratio (PRR). Results: Among 10,178,522 AE reports, 1719 reports were TAA-associated cardiac AEs as primary suspect drug. Most reports were related to fluconazole (38.34%), voriconazole (28.56%) and itraconazole (26.76%). Itraconazole (N = 195, 42.39%) and isavuconazole (N = 2, 14.29%) had fewer serious outcome events than three other drugs including fluconazole, voriconazole, and posaconazole. 13, 11, 26, 5 and 1 signals were detected for fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole, respectively. The number of new signals unrecorded in the drug label was 9, 2, 13, 2 and 0 for fluconazole, voriconazole, itraconazole, posaconazole and isavuconazole, respectively. Conclusion: Isavuconazole might be the safest of the five TAAs for cardiac AEs. TAA-associated cardiac disorders may result in serious adverse outcomes. Therefore, in addition to AEs on the drug label, we should pay attention to new AEs unrecorded on the drug label during the clinical use of TAAs.

Antibiotics-associated pseudomembranous colitis: a disproportionality analysis of the US food and drug administration adverse event reporting system (FAERS) database.

BACKGROUND: Evaluating antibiotics most commonly associated with pseudomembranous colitis (PMC) based on the real-world data is of great significance. RESEARCH DESIGN AND METHODS: We used the data from FAERS to evaluate the potential association between antibiotics and PMC by disproportionality analyzes. RESULTS: Eighty-one antibiotics which met the three algorithms simultaneously were enrolled. There were 1683 reports of PMC associated with the enrolled antibiotics. In the top 24 antibiotics, cefoxitin, streptomycin, fosfomycin, and micafungin had a high risk of PMC, but there were few reports in the literature. CONCLUSIONS: This study was of great significance for healthcare professionals to realize the potential PMC risks of antibiotics.

Intensive vision-guided network for radiology report generation.

Objective.Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis. Second, when generating reports, they overlook context reasoning with multi-modal information and focus on pure textual optimization utilizing retrieval-based methods. We aim to address these two issues by proposing a model that better simulates clinicians perspectives and generates more accurate reports.Approach.Given the above limitation in feature extraction, we propose a globally-intensive attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. GIA aims to learn three types of vision perception: depth view, space view, and pixel view. On the other hand, to address the above problem in report generation, we explore how to involve multi-modal signals to generate precisely matched reports, i.e. how to integrate previously predicted words with region-aware visual content in next word prediction. Specifically, we design a visual knowledge-guided decoder (VKGD), which can adaptively consider how much the model needs to rely on visual information and previously predicted text to assist next word prediction. Hence, our final intensive vision-guided network framework includes a GIA-guided visual encoder and the VKGD.Main results.Experiments on two commonly-used datasets IU X-RAY and MIMIC-CXR demonstrate the superior ability of our method compared with other state-of-the-art approaches.Significance.Our model explores the potential of simulating clinicians perspectives and automatically generates more accurate reports, which promotes the exploration of medical automation and intelligence.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Microneedles loaded with glutathione-scavenging composites for nitric oxide enhanced photodynamic therapy of melanoma.

Photodynamic therapy (PDT) represents an attractive promising route for melanoma treatment. However, its therapeutic efficacy is compromised by inefficient drug delivery and high glutathione (GSH) levels in cancer cells. To overcome these challenges, microneedles (MNs) system loaded with GSH-scavenging nanocomposites was presented for nitric oxide (NO) enhanced PDT. The nanocomposites consisted of S-nitroso-N-acrylate penicillamine (SNAP; a NO donor) grafted fourth-generation polyamide amine dendrimer (G4) and chlorin e6 (Ce6). Upon local insertion of polyvinylpyrrolidone MNs, G4-SNAP/Ce6 composites were fast delivered and significantly amplified the therapeutic effects during PDT, via GSH depletion and reactive nitrogen species generation. Even with a single administration and low power light exposure, MNs with G4-SNAP/Ce6 effectively halt the tumor progression. The system demonstrated better cancer ablation efficacy than Ce6 alone toward melanoma. The strategy may inspire new ideas for future PDT-related therapy for skin tumors.

A photocatalytic carbon monoxide-generating effervescent microneedle patch for improved transdermal chemotherapy.

Carbon monoxide (CO) is regarded as a promising therapeutic agent for chemotherapy sensitization. To simultaneously achieve controllable in situ CO production and efficient chemotherapeutics delivery is of great significance. Here, we presented a polyvinylpyrrolidone (PVP) core-shell microneedle (MN) system that encapsulated the effervescent component, photocatalyst, and doxorubicin hydrochloride (Dox·HCl) for CO-sensitized chemotherapy. Upon the insertion of MNs, the effervescent component, composed of sodium bicarbonate and tartaric acid, was exposed to interstitial fluid, leading to the burst release of carbon dioxide (CO2). The generated gas not only enhanced the diffusion of Dox·HCl but also served as a substrate for the photocatalytic generation of CO. From the experimental results, the photocatalyst CuS atomic layers (CAL) displayed an effective CO2 photoreduction performance, which could realize an irradiation time/intensity-dependent CO-controlled release. Ex vivo permeation studies demonstrated that effervescent CO2 production markedly enhanced the intradermal diffusion of Dox·HCl. Eventually, the robust antitumor efficacy of this versatile MN platform was proved in B16F10-bearing nude mice. This CO-sensitized chemotherapeutic MN system offered a novel strategy for transdermal gas/drug delivery, which might provide a new direction in tumor suppression.

Knowledge-Aware Global Reasoning for Situation Recognition.

The task of situation recognition aims to solve the visual reasoning problem with the ability to predict the activity happening (salient action) in an image and the nouns of all associated semantic roles playing in the activity. This poses severe challenges due to long-tailed data distributions and local class ambiguities. Prior works only propagate the local noun-level features on one single image without utilizing global information. We propose a Knowledge-aware Global Reasoning (KGR) framework to endow neural networks with the capability of adaptive global reasoning over nouns by exploiting diverse statistical knowledge. Our KGR is a local-global architecture, which consists of a local encoder to generate noun features using local relations and a global encoder to enhance the noun features via global reasoning supervised by an external global knowledge pool. The global knowledge pool is created by counting the pairwise relationships of nouns in the dataset. In this paper, we design an action-guided pairwise knowledge as the global knowledge pool based on the characteristic of the situation recognition task. Extensive experiments have shown that our KGR not only achieves state-of-the-art results on a large-scale situation recognition benchmark, but also effectively solves the long-tailed problem of noun classification by our global knowledge.

Enhanced Transcutaneous Chemodynamic Therapy for Melanoma Treatment through Cascaded Fenton-like Reactions and Nitric Oxide Delivery.

Chemodynamic therapy (CDT) has emerged as a promising strategy for cancer treatment. However, its effectiveness has been hindered by insufficient hydrogen peroxide (H2O2) and high reductive glutathione (GSH) within tumors, which are the two main reasons for the inefficiency of Fenton/Fenton-like reaction-based CDT. Herein, we present a H2O2 boost-GSH depletion strategy for enhanced CDT to fight against melanoma through a microneedle (MN)-based transcutaneous delivery method. The MN system is composed of dissolvable polyvinylpyrrolidone integrated with stimuli-responsive prodrugs. Under an intracellular acidic environment, the smart release of H2O2 boosting components is triggered, subsequently initiating nitric oxide (NO) release and enhancing the Fenton-like reaction in a cascade manner. The generation of hydroxyl radicals (•OH), along with the depletion of GSH by NO, amplifies the oxidative stress within tumor cells, promoting apoptosis and ferroptosis. The antitumor efficacy of the MN patch is validated in an A375 mouse melanoma model. This "H2O2 boost-GSH depletion-Fenton killing" strategy expands the options for superficial tumor treatment through MN-mediated enhanced CDT.

Microneedle system with light trigger for precise and programmable penetration.

The use of microneedle (MN) systems has the potential to benefit a wide range of biomedical applications but is hindered by poorly controlled insertion. Herein, a novel MN penetration strategy is presented, which utilizes the recovery stress of near-infrared light-triggered shape memory polymers (SMPs) to drive MN insertion. This strategy enables force control over MN application with the precision of 15 mN through tunable light intensity. The pre-stretch strain of SMP can be further predetermined to provide a safety margin on penetration depth. Using this strategy, we demonstrate that MN can precisely insert into the stromal layer of the rabbit cornea. Additionally, the MN unit array allows programmable insertion for multistage and patterned payload delivery. This proof-of-concept strategy promises remotely, precisely, and spatiotemporally controlled MN insertion, which may inspire the further development of MN-related applications.

Bailando++: 3D Dance GPT With Choreographic Memory.

Our proposed music-to-dance framework, Bailando++, addresses the challenges of driving 3D characters to dance in a way that follows the constraints of choreography norms and maintains temporal coherency with different music genres. Bailando++ consists of two components: a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequences, and an actor-critic Generative Pre-trained Transformer (GPT) that composes these units into a fluent dance coherent to the music. In particular, to synchronize the diverse motion tempos and music beats, we introduce an actor-critic-based reinforcement learning scheme to the GPT with a novel beat-align reward function. Additionally, we consider learning human dance poses in the rotation domain to avoid body distortions incompatible with human morphology, and introduce a musical contextual encoding to allow the motion GPT to grasp longer-term patterns of music. Our experiments on the standard benchmark show that Bailando++ achieves state-of-the-art performance both qualitatively and quantitatively, with the added benefit of the unsupervised discovery of human-interpretable dancing-style poses in the choreographic memory.

ZIC5 promotes human hepatocellular carcinoma cell proliferation through upregulating COL1A1.

Background: Zinc finger of the cerebellum 5 (ZIC5) has been found to be abnormally expressed in a variety of tumors. This study aimed to reveal the expression and functional mechanism of ZIC5 in hepatocellular carcinoma (HCC). Methods: Analysis of ZIC5 expression in various tumors and its relationship with survival were derived from The Cancer Genome Atlas (TCGA) database. Cell counting kit-8 (CCK-8) and colony formation assays were performed for the detection of HCC cell proliferation. Differentially expressed genes (DEGs) after ZIC5 overexpression in Huh1 cells were determined by RNA sequencing. Western blot assays were conducted to detect the protein levels of c-Myc, Bcl2, p21, E-cadherin, N-cadherin, vimentin, and collagen type I alpha 1 (COL1A1). Results: Dramatically increased expression of ZIC5 was observed in various tumor tissues, including HCC. Pearson's correlation analysis revealed that the mRNA levels of ZIC5 had a positive correlation with the mRNA levels of MKI67 in HCC tissues. Patients with high ZIC5 expression had a shorter overall survival time. Moreover, ZIC5 overexpression promoted HCC cell proliferation. Then, we found that COL1A1 was significantly upregulated by ZIC5 overexpression to promote the proliferation, migration, and invasion of HCC cells. Conclusions: ZIC5 could accelerate the proliferation, migration, and invasion of HCC cells through upregulating COL1A1. This is the first report that ZIC5 and COL1A1 are intrinsically linked, expanding new research ideas for subsequent HCC research.

Dissolving microneedles with a biphasic release of antibacterial agent and growth factor to promote wound healing.

Infected wound healing is a complex and dynamic process affecting millions of people. Since wound healing contains multiple stages, it requires staged management to realize the early inhibition of infection and the subsequent promotion of wound healing. A key point is to design a biphasic release system with antibacterial agents and growth factors to promote wound regeneration. As a safe, efficient and painless transdermal drug delivery method, microneedles (MNs) have attracted widespread attention. Herein, we present dissolving MNs with the biphasic release of an antibacterial agent and a growth factor to promote wound healing. bFGF was first encapsulated in PLGA microspheres (bFGF@PLGA) and then co-loaded with free ofloxacin onto polyvinylpyrrolidone MNs. Owing to the fast dissolution of the substrate, ofloxacin was quickly released to rapidly inhibit infection, while the PLGA microspheres were left in the wound. Due to the slow degradation of PLGA, bFGF encapsulated in the PLGA microspheres was slowly released to further promote wound healing. In vivo studies demonstrated that the MNs with the biphasic release of antibacterial agent and growth factor exhibited a superior capability to promote wound healing. This biphasic release system combined with microneedles has a bright future in wound healing.

Microneedle patch with "spongy coating" to co-load multiple drugs to treat multidrug-resistant melanoma.

Melanoma is the most aggressive skin malignancy that continues to increase in worldwide. The transferability and multidrug resistance lead to a high fatality rate. Synergistic administration of hydrophilic carboplatin (CBP) and hydrophobic vorinostat (SAHA) can be a reliable way to treat multidrug-resistant melanoma. However, the different physicochemical properties of multiple drugs make it difficult to achieve a convenient co-loading and an ideal synergistic treatment efficacy. To solve the problem, a microneedle patch with a porous "spongy coating" (PF-MNP) was fabricated. Firstly, (polyacrylic acid/polyethyleneimine)10 multilayers were fabricated on polymethyl methacrylate MNP. Then a "spongy coating" was achieved by acid treatment and freeze-drying. Due to the capillary effect, hydrophobic SAHA and hydrophilic CBP could be conveniently adsorbed step-by-step. The two drugs could distribute evenly on the surface, and the morphology of MNP remained good. The loading content of SAHA and CBP was easily regulated by adjusting the concentration of the adsorption solution, and MNP could quickly release most drugs within 30 min. The final in vivo experiments proved that CBP/SAHA co-loaded PF-MNP had the best therapeutic efficiency for multidrug-resistant melanoma. The MNP with a "spongy coating" showed potential to be a safe and efficient transdermal delivery platform for multiple drugs.