Development of a 3D printed perfusable in vitro blood-brain barrier model for use as a scalable screening tool.

Despite recent technological advances in drug discovery, the success rate for neurotherapeutics remains alarmingly low compared to treatments for other areas of the body. One of the biggest challenges for delivering therapeutics to the central nervous system (CNS) is the presence of the blood-brain barrier (BBB). In vitro blood-brain barrier models with high predictability are essential to aid in designing parameters for new therapeutics, assess their ability to cross the BBB, and investigate therapeutic strategies that can be employed to enhance transport. Here, we demonstrate the development of a 3D printable hydrogel blood-brain barrier model that mimics the cellular composition and structure of the blood-brain barrier with human brain endothelial cells lining the surface, pericytes in direct contact with the endothelial cells on the abluminal side of the endothelium, and astrocytes in the surrounding printed bulk matrix. We introduce a simple, static printed hemi-cylinder model to determine design parameters such as media selection, co-culture ratios, and cell incorporation timing in a resource-conservative and high-throughput manner. Presence of cellular adhesion junction, VE-Cadherin, efflux transporters, P-glycoprotein (P-gp) and Breast cancer resistance protein (BCRP), and receptor-mediated transporters, Transferrin receptor (TfR) and low-density lipoprotein receptor-related protein 1 (LRP1) were confirmed via immunostaining demonstrating the ability of this model for screening in therapeutic strategies that rely on these transport systems. Design parameters determined in the hemi-cylinder model were translated to a more complex, perfusable vessel model to demonstrate its utility for determining barrier function and assessing permeability to model therapeutic compounds. This 3D-printed blood-brain barrier model represents one of the first uses of projection stereolithography to fabricate a perfusable blood-brain barrier model, enabling the patterning of complex vessel geometries and precise arrangement of cell populations. This model demonstrates potential as a new platform to investigate the delivery of neurotherapeutic compounds and drug delivery strategies through the blood-brain barrier, providing a useful in vitro screening tool in central nervous system drug discovery and development.

PST-Diff: Achieving High-consistency Stain Transfer by Diffusion Models with Pathological and Structural Constraints.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images through the design of asymmetric attention mechanisms, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

Insight into uniform filming of LiF-rich interphase via synergistic adsorption for high-performance lithium metal anode.

Multi-scale simulation is an important basis for constructing digital batteries to improve battery design and application. LiF-rich solid electrolyte interphase (SEI) is experimentally proven to be crucial for the electrochemical performance of lithium metal batteries. However, the LiF-rich SEI is sensitive to various electrolyte formulas and the fundamental mechanism is still unclear. Herein, the structure and formation mechanism of LiF-rich SEI in different electrolyte formulas have been reviewed. On this basis, it further discussed the possible filming mechanism of LiF-rich SEI determined by the initial adsorption of the electrolyte-derived species on the lithium metal anode (LMA). It proposed that individual LiF species follow the Volmer-Weber mode of film growth due to its poor wettability on LMA. Whereas, the synergistic adsorption of additive-derived species with LiF promotes the Frank-Vander Merwe mode of film growth, resulting in uniform LiF deposition on the LMA surface. This perspective provides new insight into the correlation between high LiF content, wettability of LiF, and highperformance of uniform LiF-rich SEI. It disclosed the importance of additive assistant synergistic adsorption on the uniform growth of LiF-rich SEI, contributing to the reasonable design of electrolyte formulas and high-performance LMA, and enlightening the way for multi-scale simulation of SEI.

Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma.

Background: Hyaluronan-mediated motility receptor (HMMR) is overexpressed in multiple carcinomas and influences the development and treatment of several cancers. However, its role in hepatocellular carcinoma (HCC) remains unclear. Methods: The "limma" and "GSVA" packages in R were used to perform differential expression analysis and to assess the activity of signalling pathways, respectively. InferCNV was used to infer copy number variation (CNV) for each hepatocyte and "CellChat" was used to analyse intercellular communication networks. Recursive partitioning analysis (RPA) was used to re-stage HCC patients. The IC50 values of various drugs were evaluated using the "pRRophetic" package. In addition, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to confirm HMMR expression in an HCC tissue microarray. Flow cytometry (FCM) and cloning, Edu and wound healing assays were used to explore the capacity of HMMR to regulate HCC tumour. Results: Multiple cohort studies and qRT-PCR demonstrated that HMMR was overexpressed in HCC tissue compared with normal tissue. In addition, HMMR had excellent diagnostic performance. HMMR knockdown inhibited the proliferation and migration of HCC cells in vitro. Moreover, high HMMR expression was associated with "G2M checkpoint" and "E2F targets" in bulk RNA and scRNA-seq, and FCM confirmed that HMMR could regulate the cell cycle. In addition, HMMR was involved in the regulation of the tumour immune microenvironment via immune cell infiltration and intercellular interactions. Furthermore, HMMR was positively associated with genomic heterogeneity with patients with high HMMR expression potentially benefitting more from immunotherapy. Moreover, HMMR was associated with poor prognosis in patients with HCC and the re-staging by recursive partitioning analysis (RPA) gave a good prognosis prediction value and could guide chemotherapy and targeted therapy. Conclusion: The results of the present study show that HMMR could play a role in the diagnosis, prognosis, and treatments of patients with HCC based on bulk RNA-seq and scRAN-seq analyses and is a promising molecular marker for HCC.

Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy.

α-Enolase (ENO1) is a crucial molecular target for tumor therapy and has emerged as a research hotspot in recent decades. Here, we aimed to explore the role of ENO1 in bladder cancer (BLCA) and then construct a signature to predict the prognosis and treatment response of BLCA. Firstly, we found ENO1 was highly expressed in BLCA tissues, as verified by IHC, and was associated with poor prognosis. The analysis of the tumor immune microenvironment by bulk RNA-seq and scRNA-seq showed that ENO1 was associated with CD8+ T-cell exhaustion. Additionally, the results in vitro showed that ENO1 could promote the proliferation and invasion of BLCA cells. Then, the analysis of epithelial cells (ECs) revealed that ENO1 might promote BLCA progression by metabolism, the cell cycle and some carcinogenic pathways. A total of 249 hub genes were obtained from differentially expressed genes between ENO1-related ECs, and we used LASSO analysis to construct a novel signature that not only accurately predicted the prognosis of BLCA patients but also predicted the response to treatment for BLCA. Finally, we constructed a nomogram to better guide clinical application. In conclusion, through multi-omics analysis, we found that ENO1 was overexpressed in bladder cancer and associated with poor prognosis, CD8+ T-cell exhaustion and epithelial heterogeneity. Moreover, the prognosis and treatment of patients can be well predicted by constructing an epithelial-related prognostic signature.

SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification.

BACKGROUND AND OBJECTIVE: The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. METHODS: To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. RESULTS: Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. CONCLUSIONS: By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.

FRESH™ 3D bioprinted cardiac tissue, a bioengineered platform for in vitro pharmacology.

There is critical need for a predictive model of human cardiac physiology in drug development to assess compound effects on human tissues. In vitro two-dimensional monolayer cultures of cardiomyocytes provide biochemical and cellular readouts, and in vivo animal models provide information on systemic cardiovascular response. However, there remains a significant gap in these models due to their incomplete recapitulation of adult human cardiovascular physiology. Recent efforts in developing in vitro models from engineered heart tissues have demonstrated potential for bridging this gap using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in three-dimensional tissue structure. Here, we advance this paradigm by implementing FRESH™ 3D bioprinting to build human cardiac tissues in a medium throughput, well-plate format with controlled tissue architecture, tailored cellular composition, and native-like physiological function, specifically in its drug response. We combined hiPSC-CMs, endothelial cells, and fibroblasts in a cellular bioink and FRESH™ 3D bioprinted this mixture in the format of a thin tissue strip stabilized on a tissue fixture. We show that cardiac tissues could be fabricated directly in a 24-well plate format were composed of dense and highly aligned hiPSC-CMs at >600 million cells/mL and, within 14 days, demonstrated reproducible calcium transients and a fast conduction velocity of ∼16 cm/s. Interrogation of these cardiac tissues with the ß-adrenergic receptor agonist isoproterenol showed responses consistent with positive chronotropy and inotropy. Treatment with calcium channel blocker verapamil demonstrated responses expected of hiPSC-CM derived cardiac tissues. These results confirm that FRESH™ 3D bioprinted cardiac tissues represent an in vitro platform that provides data on human physiological response.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making.

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

PPG-based Continuous BP Waveform Estimation Using Polarized Attention-guided Conditional Adversarial Learning Model.

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.

Three-Dimensional Bioprinting of Human iPSC-Derived Neuron-Astrocyte Cocultures for Screening Applications.

For a cell model to be viable for drug screening, the system must meet throughput and homogeneity requirements alongside having an efficient development time. However, many published 3D models do not satisfy these criteria. This therefore, limits their usefulness in early drug discovery applications. Three-dimensional (3D) bioprinting is a novel technology that can be applied to the development of 3D models to expedite development time, increase standardization, and increase throughput. Here, we present a protocol to develop 3D bioprinted coculture models of human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons and astrocytes. These cocultures are embedded within a hydrogel matrix of bioactive peptides, full-length extracellular matrix (ECM) proteins, and with a physiological stiffness of 1.1 kPa. The model can be rapidly established in 96-well and 384-well formats and produces an average post-print viability of 72%. The astrocyte-to-neuron ratio in this model is shown to be 1:1.5, which is within the physiological range for the human brain. These 3D bioprinted cell populations also show expression of mature neural cell type markers and growth of neurite and astrocyte projections within 7 days of culture. As a result, this model is suitable for analysis using cell dyes and immunostaining techniques alongside neurite outgrowth assays. The ability to produce these physiologically representative models at scale makes them ideal for use in medium-to-high throughput screening assays for neuroscience targets.

A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy.

Objective.Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of tumor probes in small animals. However, traditional deep learning reconstruction methods that aim to minimize the mean squared error (MSE) and iterative regularization algorithms that rely on optimal parameters are typically influenced by strong noise, resulting in poor FMT reconstruction robustness.Approach.In this letter, we propose an adaptive adversarial learning strategy (3D-UR-WGAN) to achieve robust FMT reconstructions. Unlike the pixel-based MSE criterion in traditional CNNs or the regularization strategy in iterative solving schemes, the reconstruction strategy can greatly facilitate the performance of the network models through alternating loop training of the generator and the discriminator. Second, the reconstruction strategy combines the adversarial loss in GANs with the L1 loss to significantly enhance the robustness and preserve image details and textual information.Main results.Both numerical simulations and physical phantom experiments demonstrate that the 3D-UR-WGAN method can adaptively eliminate the effects of different noise levels on the reconstruction results, resulting in robust reconstructed images with reduced artifacts and enhanced image contrast. Compared with the state-of-the-art methods, the proposed method achieves better reconstruction performance in terms of target shape recovery and localization accuracy.Significance.This adaptive adversarial learning reconstruction strategy can provide a possible paradigm for robust reconstruction in complex environments, and also has great potential to provide an alternative solution for solving the problem of poor robustness encountered in other optical imaging modalities such as diffuse optical tomography, bioluminescence imaging, and Cherenkov luminescence imaging.

D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain.

BACKGROUND AND OBJECTIVE: Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of fluorescent probes in small animals. Over the past few years, learning-based FMT reconstruction methods have achieved promising results. However, these methods typically attempt to minimize the mean-squared error (MSE) between the reconstructed image and the ground truth. Although signal-to-noise ratios (SNRs) are improved, they are susceptible to non-uniform artifacts and loss of structural detail, making it extremely challenging to obtain accurate and robust FMT reconstructions under noisy measurements. METHODS: We propose a novel dual-domain joint strategy based on the image domain and perception domain for accurate and robust FMT reconstruction. First, we formulate an explicit adversarial learning strategy in the image domain, which greatly facilitates training and optimization through two enhanced networks to improve anti-noise ability. Besides, we introduce a novel transfer learning strategy in the perceptual domain to optimize edge details by providing perceptual priors for fluorescent targets. Collectively, the proposed dual-domain joint reconstruction strategy can significantly eliminate the non-uniform artifacts and effectively preserve the structural edge details. RESULTS: Both numerical simulations and in vivo mouse experiments demonstrate that the proposed method markedly outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. CONCLUSIONS: The proposed method achieves the best reconstruction performance and has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.

Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography.

Limited-projection fluorescence molecular tomography (FMT) allows rapid reconstruction of the three-dimensional (3D) distribution of fluorescent targets within a shorter data acquisition time. However, the limited-projection FMT is severely ill-posed and ill-conditioned due to insufficient fluorescence measurements and the strong scattering properties of photons in biological tissues. Previously, regularization-based methods, combined with the sparse distribution of fluorescent sources, have been commonly used to alleviate the severe ill-posed nature of the limited-projection FMT. Due to the complex iterative computations, time-consuming solution procedures, and less stable reconstruction results, the limited-projection FMT remains an intractable challenge for achieving fast and accurate reconstructions. In this work, we completely discard the previous iterative solving-based reconstruction themes and propose multi-branch attention prior based parameterized generative adversarial network (MAP-PGAN) to achieve fast and accurate limited-projection FMT reconstruction. Firstly, the multi-branch attention can provide parameterized weighted sparse prior information for fluorescent sources, enabling MAP-PGAN to effectively mitigate the ill-posedness and significantly improve the reconstruction accuracy of limited-projection FMT. Secondly, since the end-to-end direct reconstruction strategy is adopted, the complex iterative computation process in traditional regularization algorithms can be avoided, thus greatly accelerating the 3D visualization process. The numerical simulation results show that the proposed MAP-PGAN method outperforms the state-of-the-art methods in terms of localization accuracy and morphological recovery. Meanwhile, the reconstruction time is only about 0.18s, which is about 100 to 1000 times faster than the conventional iteration-based regularization algorithms. The reconstruction results from the physical phantoms and in vivo experiments further demonstrate the feasibility and practicality of the MAP-PGAN method in achieving fast and accurate limited-projection FMT reconstruction.

In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography.

SIGNIFICANCE: Pharmacokinetic parametric images in dynamic fluorescence molecular tomography (FMT) can describe three-dimensional (3D) physiological and pathological information inside biological tissues, potentially providing quantitative assessment tools for biological research and drug development. AIM: In vivo imaging of the liver tumor with pharmacokinetic parametric images from dynamic FMT based on the differences in metabolic properties of indocyanine green (ICG) between normal liver cells and tumor liver cells inside biological tissues. APPROACH: First, an orthotopic liver tumor mouse model was constructed. Then, with the help of the FMT/computer tomography (CT) dual-modality imaging system and the direct reconstruction algorithm, 3D imaging of liver metabolic parameters in nude mice was achieved to distinguish liver tumors from normal tissues. Finally, pharmacokinetic parametric imaging results were validated against in vitro anatomical results. RESULTS: This letter demonstrates the ability of dynamic FMT to monitor the pharmacokinetic delivery of the fluorescent dye ICG in vivo, thus, enabling the distinction between normal and tumor tissues based on the pharmacokinetic parametric images derived from dynamic FMT. CONCLUSIONS: Compared with CT structural imaging technology, dynamic FMT combined with compartmental modeling as an analytical method can obtain quantitative images of pharmacokinetic parameters, thus providing a more powerful research tool for organ function assessment, disease diagnosis and new drug development.

Regulation of Dendrite-Free Li Plating via Lithiophilic Sites on Lithium-Alloy Surface.

Lithium (Li) deposition behavior plays an important role in dendrite formation and the subsequent performance of lithium metal batteries. This work reveals the impact of the lithiophilic sites of lithium-alloy on the Li plating process via the first-principles calculations. We find that the Li deposition mechanisms on the Li metal and Li22Sn5 surface are different due to the lithiophilic sites. We first propose that Li plating on the Li metal surface goes through the "adsorption-reduction-desorption-heterogeneous nucleation-cluster drop" process, while it undergoes the "adsorption-reduction-growth" process on the Li22Sn5 surface. The lower adsorption energy contributes to the easy adsorption of Li on the lithiophilic sites of the Li22Sn5 surface. The lower Li reduction energy on the Li metal surface indicates that it is easy for Li to be reduced on the Li metal surface, attributed to its higher Fermi energy level. Furthermore, the faster Li diffusion on the Li22Sn5 surface results in smooth Li deposition, which is based on a "two-Li synergy diffusion" mechanism. However, Li diffuses more slowly on the Li metal surface than on the Li22Sn5 surface due to the "single Li diffusion" mechanism. This work provides a fundamental understanding on the impact of lithiophilic sites of Li alloy on the Li plating process and points out that the future design of 3D Li-alloy substrates decorated with multilithiophilic sites can prevent dendrite formation on the lithium-alloy substrate by guiding uniform Li deposition.

A 3D printable perfused hydrogel vascular model to assay ultrasound-induced permeability.

The development of an in vitro model to study vascular permeability is vital for clinical applications such as the targeted delivery of therapeutics. This work demonstrates the use of a perfusion-based 3D printable hydrogel vascular model as an assessment for endothelial permeability and its barrier function. Aside from providing a platform that more closely mimics the dynamic vascular conditions in vivo, this model enables the real-time observation of changes in the endothelial monolayer during the application of ultrasound to investigate the downstream effect of ultrasound-induced permeability. We show an increase in the apparent permeability coefficient of a fluorescently labeled tracer molecule after ultrasound treatment via a custom MATLAB algorithm, which implemented advanced features such as edge detection and a dynamic region of interest, thus supporting the use of ultrasound as a non-invasive method to enhance vascular permeability for targeted drug therapies. Notably, live-cell imaging with VE-cadherin-GFP HUVECs provides some of the first real-time acquisitions of the dynamics of endothelial cell-cell junctions under the application of ultrasound in a 3D perfusable model. This model demonstrates potential as a new scalable platform to investigate ultrasound-assisted delivery of therapeutics across a cellular barrier that more accurately mimics the physiologic matrix and fluid dynamics.

A positive feedback circuit comprising p21 and HIF-1α aggravates hypoxia-induced radioresistance of glioblastoma by promoting Glut1/LDHA-mediated glycolysis.

The radioresistance induced by hypoxia is the major obstacle in the successful treatment of cancer radiotherapy. p21 was initially identified as a widespread inhibitor of cyclin-dependent kinases, through which mediates the p53-dependent cell cycle G1 phase arrest in response to a variety of stress stimuli. In this study, we discovered a novel function of p21, which participated in the regulation of metabolic pathways under hypoxia. We found that p21 was upregulated in glioblastoma (GBM) cells under hypoxic conditions, which enhanced the radioresistance of GBM cells. In principle, HIF-1α is bound directly to the hypoxia response elements (HREs) of the p21 promoter to enhance its transcription activity, in turn, p21 also promoted the transcription of HIF-1α at the mRNA level and maintained HIF-1α function under oxygen deficiency. The positive correlation between p21 and HIF-1α augmented Glut1/LDHA-mediated glycolysis and aggravated the radioresistance of GBM cells. Thus, our results constructed a positive feedback circuit comprising p21/HIF-1α that might play a key role in enhancing the radioresistance of GBM under hypoxia.

Advances in Treatment Models of Advanced Gastric Cancer.

The prognosis of advanced gastric cancer (AGC) is extremely poor, and the therapeutic effect of traditional palliative chemotherapy is far from satisfactory. To overcome this bottleneck, palliative surgery resection, perioperative chemotherapy combined with surgical resection, hyperthermic intraperitoneal chemotherapy (HIPEC), pressurized intraperitoneal aerosol chemotherapy (PIPAC), radiation therapy, molecular-targeted therapy have been explored in AGC. Although considerable progress has been achieved, there is still no overwhelming therapeutic method. Due to the high heterogeneity of AGC, it is particularly vital to reshaped the paradigm of gastric cancer therapy according to the characteristics of clinical classifications and molecular subtypes.

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

Importance: Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking. Objective: To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer. Design, Setting, and Participants: This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020. Exposure: Clinical and DCE-MRI radiomic signatures. Main Outcomes and Measures: The primary end points were ALNM and DFS. Results: This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone. Conclusions and Relevance: This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.